Messaging search and management apparatuses, methods and systems

ABSTRACT

The Messaging Search and Management Apparatuses, Methods and Systems (“MSM”) transforms message, ranking request inputs via MSM components into work graphs, ML structure input data, ML structure, ranking response outputs. A work graph generation request that includes group level access control data may be obtained. A set of metadata access control carrying messages, a set of users, a set of channels, and a set of topics with access control data corresponding to the group level access control data may be determined. A user priority score for each of the other users, a channel priority score for each of the channels, and a topic priority score for each of the topics, from the perspective of each user, may be calculated. A work graph data structure may be generated that includes, for each user, data regarding the calculated user priority scores, channel priority scores, and topic priority scores.

This application for letters patent disclosure document describesinventive aspects that include various novel innovations (hereinafter“disclosure”) and contains material that is subject to copyright, maskwork, and/or other intellectual property protection. The respectiveowners of such intellectual property have no objection to the facsimilereproduction of the disclosure by anyone as it appears in publishedPatent Office file/records, but otherwise reserve all rights.

PRIORITY CLAIM

This application is a continuation of and claims priority under U.S.C.120 to U.S. application Ser. No. 15/651,887, filed Jul. 17, 2017 titled“Messaging Search and Management Apparatuses, Methods and Systems” whichis a continuation-in-part of and claims priority under 35 U.S.C. § 120to (1) U.S. application Ser. No. 15/604,584, filed May 24, 2017, titled“Messaging Search and Management Apparatuses, Methods and Systems,”which in turn claims priority under 35 U.S.C. § 119 to US provisionalpatent applications: Ser. No. 62/408,670, filed Oct. 14, 2016, entitled“Messaging Search and Management Apparatuses, Methods and Systems,” andSer. No. 62/500,451, filed May 2, 2017, entitled “Messaging Search andManagement Apparatuses, Methods and Systems”; and to (2) U.S.Application to: Ser. No. 15/604,589 titled “Messaging Search andManagement Apparatuses, Methods and Systems,” filed on May 24, 2017,which in turn claims priority under 35 U.S.C. § 119 to U.S. provisionalpatent applications: Ser. No. 62/408,670, filed Oct. 14, 2016, entitled“Messaging Search and Management Apparatuses, Methods and Systems,” andSer. No. 62/500,451, filed May 2, 2017, entitled “Messaging Search andManagement Apparatuses, Methods and Systems”. This application is acontinuation in part of and claims priority under U.S.C. 120 to U.S.application Ser. No. 15/782,678, filed Oct. 12, 2017, titled “Method,Apparatus, and Computer Program Product For Associating an IdentifierWith One or More Message Communications Within a Group-BasedCommunication System,” which in turn claims priority under 35 U.S.C. §119 to U.S. provisional patent applications: Ser. No. 62/408,670, filedOct. 14, 2016, entitled “Messaging Search and Management Apparatuses,Methods and Systems,” and Ser. No. 62/556,606, filed Sep. 11, 2017,entitled “Method, Apparatus, and Computer Program Product ForAssociating an Identifier With One or More Message Communications Withina Group-Based Communication System”. This application is also acontinuation in part of and claims priority under U.S.C. 120 to U.S.application Ser. No. 15/782,680, filed Oct. 12, 2017, titled “Method,Apparatus, and Computer Program Product For Associating an IdentifierWith One or More Message Communications Within a Group-BasedCommunication System,” which in turn claims priority under 35 U.S.C. §119 to U.S. provisional patent applications: Ser. No. 62/408,670, filedOct. 14, 2016, entitled “Messaging Search and Management Apparatuses,Methods and Systems,” and Ser. No. 62/554,952, filed Sep. 6, 2017,entitled “Method, Apparatus, and Computer Program Product ForAssociating an Identifier With One or More Message Communications Withina Group-Based Communication System”.

The entire contents of the aforementioned applications are hereinexpressly incorporated by reference.

FIELD

The present innovations generally address internet messaging, and moreparticularly, include Messaging Search and Management Apparatuses,Methods and Systems.

However, in order to develop a reader's understanding of theinnovations, disclosures have been compiled into a single description toillustrate and clarify how aspects of these innovations operateindependently, interoperate as between individual innovations, and/orcooperate collectively. The application goes on to further describe theinterrelations and synergies as between the various innovations; all ofwhich is to further compliance with 35 U.S.C. § 112.

BACKGROUND

The internet allows for various communication forms such as email, filetransfer protocols, and messaging. Various types of messaging existincluding Internet Relay Chat, AOL Instant Messenger, Apple's iMessage,all of which allow users to send and receive textual messages.

BRIEF DESCRIPTION OF THE DRAWINGS

Appendices and/or drawings illustrating various, non-limiting, example,innovative aspects of the Messaging Search and Management Apparatuses,Methods and Systems (hereinafter “MSM”) disclosure, include:

FIG. 1 shows an exemplary architecture for the MSM;

FIG. 2 shows a datagraph diagram illustrating embodiments of a metadatadetermining data flow for the MSM;

FIG. 3 shows a logic flow diagram illustrating embodiments of a metadatadetermining (MD) component for the MSM;

FIG. 4 shows a datagraph diagram illustrating embodiments of a workgraph and machine learning (ML) structure generating data flow for theMSM;

FIG. 5 shows a logic flow diagram illustrating embodiments of a workgraph generating (WGG) component for the MSM;

FIG. 6 shows an exemplary work graph for the MSM;

FIG. 7 shows a logic flow diagram illustrating embodiments of a MLstructure generating (MLSG) component for the MSM;

FIG. 8 shows a datagraph diagram illustrating embodiments of a rankingdata flow for the MSM;

FIG. 9 shows a logic flow diagram illustrating embodiments of a rankingdetermining (RD) component for the MSM;

FIG. 10 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 11 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 12 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 13 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 14 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 15 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 16 shows a screenshot diagram illustrating embodiments of the MSM;

FIGS. 17A-17C show screenshot diagrams illustrating embodiments of theMSM;

FIG. 18 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 19 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 20 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 21 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 22 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 23 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 24 shows a screenshot diagram illustrating embodiments of the MSM;

FIG. 25 shows a screenshot diagram illustrating embodiments of the MSM;and

FIG. 26 shows a block diagram illustrating embodiments of a MSMcontroller;

Generally, the leading number of each citation number within thedrawings indicates the figure in which that citation number isintroduced and/or detailed. As such, a detailed discussion of citationnumber 101 would be found and/or introduced in FIG. 1. Citation number201 is introduced in FIG. 2, etc. Any citation and/or reference numbersare not necessarily sequences but rather just example orders that may berearranged and other orders are contemplated.

DETAILED DESCRIPTION

The Messaging Search and Management Apparatuses, Methods and Systems(hereinafter “MSM”) transforms message, ranking request inputs, via MSMcomponents (e.g., MD, WGG, MLSG, RD, etc. components), into work graphs,ML structure input data, ML structure, ranking response outputs. The MSMcomponents, in various embodiments, implement advantageous features asset forth below.

INTRODUCTION

The MSM may generate and associate metadata with messages to facilitatemore facets of searching. In one implementation, a facet is a structuredmetadata field you can attach to a document in a search index and filterover. The MSM may utilize message metadata to generate work graphs thatcapture relationships between users, between users and channels, betweenusers and topics, between channels and topics, between channels, betweentopics, and/or the like. Work graph data may be used as machine learning(ML) structure inputs for training and/or utilizing ML structures (e.g.,logistic regressions, neural networks, etc.). The MSM may utilizemessage metadata and/or ML structures to rank messages, people,channels, and/or the like for a variety of applications. For example,such applications may include determining relevant messages,conversations, files, people (e.g., experts who can answer a question),channels (e.g., where a question may be answered), and/or the like inresponse to a user's search query; generating a recap of a channel;ranking the most important messages to read across every channel;suggesting channels to join, leave, star, and/or the like; providing apush notification of a specified number of the most important messagesfor a user for the day.

MSM

FIG. 1 shows an exemplary architecture for the MSM. In FIG. 1, a usermay utilize a client 102 to send a user MSM message 131 (e.g., a chatmessage sent to a channel). In one embodiment, a MSM message is amessage that includes metadata that describes message context and/orfacilitates access control. In various implementations, a MSM messagemay include text, emojis, images, links, files, and/or the like. Amessage server 106 may obtain the message and send a content MSM message135 to other users, who are authorized recipients of the message (e.g.,other users on the user's team who joined the channel), utilizingclients 110 a-110 n. The content MSM message may facilitate displayingthe content of the user's message to the other users. The message servermay also send a forward MSM message 139 to a MSM server 114. The forwardMSM message may facilitate analyzing the user's message for indexing,work graph generation, ML, ranking, and/or the like purposes.

The MSM server may include a variety of modules to analyze MSM messages.In one implementation, such modules may include a metadata determiningprocess (e.g., to determine and/or facilitate indexing of messagecontents and/or metadata), a message aggregating process (e.g., tocollect and/or forward messages for further analysis), a ranking process(e.g., to facilitate ranking for a variety of applications), and/or thelike. The MSM server may facilitate indexing message contents and/ormetadata (e.g., team, channel, user, topics, responses, files, thirdparty metadata) in message indexing 120. If the user attached a file tothe message, the MSM server may facilitate indexing file contents infile indexing 122. The MSM server may facilitate storing the storage MSMmessage 143 in a MSM message database 124. The MSM message database mayperiodically (e.g., nightly, hourly) forward newly received storage MSMmessages 147 to a MSM data warehouse 128.

The MSM data warehouse may include a variety of modules to analyze MSMmessages and/or other data. In one implementation, such modules mayinclude a work graph generating process (e.g., to generate work graphs(e.g., ML structure input data such as a channel's priority for theuser)), a machine learning process (e.g., to generate other ML structureinput data (e.g., team-level term priority), to generate ML structures(e.g., team-level neural networks)), and/or the like. For example, theMSM data warehouse may utilize tools such as Apache Hive, Presto, ApacheSpark, and/or the like to facilitate analyzing MSM messages and/or otherdata. The MSM data warehouse may send ML structure parameters 151 (e.g.,parameters that define a neural network) to the MSM server for use inranking. The MSM data warehouse may facilitate indexing generated MLstructure input data 155 (e.g., in message indexing).

The user may utilize the client 102 to send a ranking request 161 to theMSM server. For example, the ranking request may be a search requestthat includes a search term (e.g., “patents”) specified by the user. TheMSM server may send a ranking data request 163 to message indexingand/or file indexing to obtain relevant messages and/or files associatedwith the search. The ranking data request may also specify ML structureinput data to obtain for ML structure(s) (e.g., different ML structuresmay be used for different types (e.g., messages, people, channels) ofresponses to the search request) utilized for ranking (e.g., for theuser's team). A ranking data response 165 may provide the requestedrelevant messages, relevant files, ML structure input data, and/or thelike to the MSM server. The MSM server may utilize ML structure(s) torank the relevant messages and/or files, people, channels, and/or thelike using ML structure input data. The MSM server may send a rankingresponse 167 to provide the highest ranked messages, files, people,channels, and/or the like to the user.

FIG. 2 shows a datagraph diagram illustrating embodiments of a metadatadetermining data flow for the MSM. In FIG. 2, dashed lines indicate dataflow elements that may be more likely to be optional. In FIG. 2, aclient 202 may send a MSM message 221 to a MSM server 206. In variousimplementations, the message may be sent to the MSM server directly bythe client, the message may be sent to the MSM server via anintermediary such as a message server, and/or the like. For example, theclient may be a desktop, a laptop, a tablet, a smartphone, and/or thelike that is executing a client application (e.g., a workgroup chatapp). In one implementation, the message may include data such as amessage identifier, user account details, a team identifier, a channelidentifier, contents (e.g., text, emojis, images, links), attachments(e.g., files), message hierarchy data (e.g., the message may be a replyto another message), third party metadata, and/or the like. In oneembodiment, the client may provide the following example message,substantially in the form of a (Secure) Hypertext Transfer Protocol(“HTTP(S)”) POST message including eXtensible Markup Language (“XML”)formatted data, as provided below:

POST /authrequest.php HTTP/1.1 Host: www.server.com Content-Type:Application/XML Content-Length: 667 <?XML version = “1.0” encoding =“UTF-8”?> <auth_request>  <timestamp>2020-12-31 23:59:59</timestamp> <user_accounts_details>     <user_account_credentials>       <user_name>ID_user_1</user_name>       <password>abc123</password>        //OPTIONAL<cookie>cookieID</cookie>        //OPTIONAL<digital_cert_link>www.mydigitalcertificate.com/JohnDoeDaDoeDoe@gmail.com/mycertifcate.dc</digital_cert_link>       //OPTIONAL <digital_certificate>_DATA_</digital_certificate>    </user_account_credentials>  </user_accounts_details> <client_details> //iOS Client with App and Webkit        //it should benoted that although several client details        //sections areprovided to show example variants of client        //sources, furthermessages will include only on to save        //space    <client_IP>10.0.0.123</client_IP>     <user_agent_string>Mozilla/5.0(iPhone; CPU iPhone OS 7_1_1 like Mac OS X) AppleWebKit/537.51.2 (KHTML,like Gecko) Version/7.0 Mobile/11D201 Safari/9537.53</user_agent_string>    <client_product_type>iPhone6,1</client_product_type>    <client_serial_number>DNXXX1X1XXXX</client_serial_number> <client_UDID>3XXXXXXXXXXXXXXXXXXXXXXXXD</client_UDID>    <client_OS>iOS</client_OS>    <client_OS_version>7.1.1</client_OS_version>    <client_app_type>app with webkit</client_app_type>    <app_installed_flag>true</app_installed_flag>     <app_name>Error!Reference source not found..app</app_name>     <app_version>1.0</app_version>     <app_webkit_name>Mobile Safari</client_webkit_name>    <client_version>537.51.2</client_version>  </client_details> <client_details> //iOS Client with Webbrowser    <client_IP>10.0.0.123</client_IP>     <user_agent_string>Mozilla/5.0(iPhone; CPU iPhone OS 7_1_1 like Mac OS X) AppleWebKit/537.51.2 (KHTML,like Gecko) Version/7.0 Mobile/11D201 Safari/9537.53</user_agent_string>    <client_product_type>iPhone6,1</client_product_type>    <client_serial_number>DNXXX1X1XXXX</client_serial_number> <client_UDID>3XXXXXXXXXXXXXXXXXXXXXXXXD</client_UDID>    <client_OS>iOS</client_OS>    <client_OS_version>7.1.1</client_OS_version>    <client_app_type>web browser</client_app_type>    <client_name>Mobile Safari</client_name>    <client_version>9537.53</client_version>  </client_details> <client_details> //Android Client with Webbrowser    <client_IP>10.0.0.123</client_IP>     <user_agent_string>Mozilla/5.0(Linux; U; Android 4.0.4; en-us; Nexus S Build/IMM76D)AppleWebKit/534.30 (KHTML, like Gecko) Version/4.0 MobileSafari/534.30</user_agent_string>     <client_product_type>NexusS</client_product_type>    <client_serial_number>YXXXXXXXXZ</client_serial_number>    <client_UDID>FXXXXXXXXX-XXXX-XXXX-XXXX- XXXXXXXXXXXXX</client_UDID>    <client_OS>Android</client_OS>    <client_OS_version>4.0.4</client_OS_version>    <client_app_type>web browser</client_app_type>    <client_name>Mobile Safari</client_name>    <client_version>534.30</client_version>  </client_details> <client_details> //Mac Desktop with Webbrowser    <client_IP>10.0.0.123</client_IP>     <user_agent_string>Mozilla/5.0(Macintosh; Intel Mac OS X 10_9_3) AppleWebKit/537.75.14 (KHTML, likeGecko) Version/7.0.3 Safari/537.75.14</user_agent_string>    <client_product_type>MacPro5,1</client_product_type>    <client_serial_number>YXXXXXXXXZ</client_serial_number>    <client_UDID>FXXXXXXXXX-XXXX-XXXX-XXXX- XXXXXXXXXXXXX</client_UDID>    <client_OS>Mac OS X</client_OS>    <client_OS_version>10.9.3</client_OS_version>    <client_app_type>web browser</client_app_type>    <client_name>Mobile Safari</client_name>    <client_version>537.75.14</client_version>  </client_details> <message>     <message_identifier>ID_message_10</message_identifier>    <team_identifier>ID_team_1</team_identifier>    <channel_identifier>ID_channel_1</channel_identifier>    <contents>That is an interesting disclosure. I have attached a copyour patent policy.</contents>    <attachments>patent_policy.pdf</attachments>  </message></auth_request>

In another embodiment, the client may provide the following examplemessage, al substantially in the form of a JSON message:

{  ″text″: ″That is an interesting invention. I have attached a copy ourpatent policy.″,  ″attachments″: [   {    ″title″: ″Patent Policy″,   ″title_link″: ″patent_policy.pdf″,    ″fields″: [     {      ″title″:″Part″,      ″value″: ″1″,      ″short″: true     },     {      ″title″:″Version″,      ″value″: ″3″,      ″short″: true     }    ],   ″author_name″: ″Patent Counsel″,    ″author_icon″:″http://a.slack-edge.com/7f18https://a.slack-edge.com/img/patent_counsel.png″,    ″image_url″:″http://i.imgur.com/patent_policy.jpg″   },   {    ″title″: ″Synopsis″,   ″text″: ″The latest version of the Patent Policy updated by@patent_counsel″   },   {    ″fallback″: ″Is this document helpful?″,   ″title″: ″Is this document helpful?″,    ″callback_id″:″pp_1234_xyz″,    ″color″: ″#3AA3E3″,    ″attachment_type″: ″default″,   ″actions″: [     {      ″name″: ″yes″,      ″text″: ″Yes″,     ″type″: ″button″,      ″value″: ″good″     },     {      ″name″:″no″,      ″text″: ″No″,      ″type″: ″button″,      ″value″: ″bad″    }    ]   }  ] }

A metadata determining (MD) component 225 may be used to analyze the MSMmessage sent to the MSM server to facilitate message indexing, fileindexing, message storage, and/or the like. See FIG. 3 for additionaldetails regarding the MD component.

The MSM server may send a storage MSM message 229 to message indexing210 to facilitate message indexing. In one implementation, the storageMSM message may include data such as a message identifier, a teamidentifier, a channel identifier, a sending user identifier, topics,responses, contents, attachments, message hierarchy data, third partymetadata, conversation primitive data, and/or the like. For example, theMSM server may provide the following example storage MSM message,substantially in the form of a HTTP(S) POST message includingXML-formatted data, as provided below:

POST /storage_message.php HTTP/1.1 Host: www.server.com Content-Type:Application/XML Content-Length: 667 <?XML version = “1.0” encoding =“UTF-8”?> <storage_message> <message_identifier>ID_message_10</message_identifier> <team_identifier>ID_team_1</team_identifier> <channel_identifier>ID_channel_1</channel_identifier> <sending_user_identifier>ID_user_1</sending_user_identifier>  <topics>  <topic>inventions</topic>   <topic>patents</topic>  <topic>policies</topic>  </topics>  <responses>   <response>liked byID_user_2</response>   <response>starred by ID_user_3</response> </responses>  <contents>That is an interesting invention. I haveattached a copy our patent policy.</contents> <attachments>patent_policy.pdf</attachments>  <conversation_primitive>  conversation includes messages: ID_message_8, ID_message_9,ID_message_10,   ID_message_11, ID_message_12  </conversation_primitive></storage_message>

The MSM server may send files 233 to file indexing 214 to facilitatefile indexing. In one implementation, files 233 may include filecontents of attachments associated with the MSM message. The MSM servermay send a storage MSM message 237 to a MSM message database 218 tofacilitate message storage.

FIG. 3 shows a logic flow diagram illustrating embodiments of a metadatadetermining (MD) component for the MSM. In FIG. 3, a MSM message may beobtained at 301. For example, a user's MSM message may be obtained foranalysis.

A team associated with the message may be determined at 305. In oneembodiment, MSM users may be organized into organization groups (e.g.,employees of each company may be a separate organization group) and eachorganization group may have one or more team groups (teams) to whichusers may be assigned or which the users may join (e.g., teams mayrepresent departments, geographic locations such as offices, productlines, and/or the like). See FIG. 10 for additional details regardingteams. In one implementation, the message may be parsed (e.g., using PHPcommands) to determine a team identifier of the team associated with themessage. For example, the team identifier may be used to facilitateaccess control for the message (e.g., access to the message, such asduring searches, may be restricted to users who are part of the team).In another example, the team identifier may be used to determine contextfor the message (e.g., a description of the team, such as the name of adepartment (e.g., engineering, accounting, legal), may be associatedwith the team identifier).

A channel associated with the message may be determined at 309. In oneembodiment, MSM users may join channels (e.g., chat rooms that they findinteresting). For example, some channels may be globally accessible toanyone in the company. In another example, access to some channels maybe restricted to members of specified teams. See FIG. 11 for additionaldetails regarding channels. In one implementation, the message may beparsed (e.g., using PHP commands) to determine a channel identifier ofthe channel where the message was posted. For example, the channelidentifier may be used to facilitate access control for the message(e.g., access to the message, such as during searches, may be restrictedto users who joined the channel or who are allowed to join the channel).In another example, the channel identifier may be used to determinecontext for the message (e.g., a description of the channel, such as adescription of a project discussed in the channel, may be associatedwith the channel identifier).

A user associated with the message may be determined at 313. In oneimplementation, the message may be parsed (e.g., using PHP commands) todetermine a user identifier of the user who sent the message. Forexample, a user (or a channel) may be thought of as a collection ofmessages associated with (e.g., sent) the user (or the channel). Thesemessages may be analyzed to determine context regarding the user (e.g.,the user's expertise in a topic may be determined based on the frequencyof mention of the topic by the user).

Topics associated with the message may be determined at 317. In oneimplementation, the message may be parsed (e.g., using PHP commands) todetermine topics discussed in the message. For example, hashtags in themessage may indicate topics associated with the message. In anotherexample, the message may be analyzed (e.g., by itself, with othermessages in a conversation primitive) using a machine learningtechnique, such as topic modeling, to determine topics associated withthe message.

Responses associated with the message may be determined at 321. Forexample, responses to the message by other users may include reactions(e.g., emoji, liking), clicking on a link in the message, replying tothe message, downloading a file associated with the message, sharing themessage from one channel to another channel, pinning the message,starring the message, and/or the like. In one implementation, dataregarding responses to the message by other users may be included withthe message, and the message may be parsed (e.g., using PHP commands) todetermine the responses. In another implementation, data regardingresponses to the message may be retrieved from a database. For example,data regarding responses to the message may be retrieved via a MySQLdatabase command similar to the following:

-   -   SELECT messageResponses    -   FROM MSM_Message    -   WHERE messageID=ID_message_10;

For example, data regarding responses to the message may be analyzed todetermine context for the message (e.g., a social score for the messagefrom the perspective of some user). In another example, data regardingresponses to the message may be analyzed to determine context regardingthe user (e.g., the user's expertise in a topic may be determined basedon the responses to the user's message regarding the topic).

A determination may be made at 325 whether attachments are included withthe message. In various implementations, attachments may include filesattached to the message, links (e.g., to webpages) in the message, filesfrom third part providers (e.g., links to G Suite files, links toDropbox files), and/or the like. If there are attachments, filesassociated with the message may be determined at 329. In oneimplementation, the message may be parsed (e.g., using PHP commands) todetermine file names, link addresses, and/or the like of theattachments. For example, file contents (e.g., contents of the attachedfiles, contents of webpages, contents of Dropbox files obtained withpermission from the user using the user's authentication information)may be analyzed to determine context for the message (e.g., a patentpolicy document may indicate that the message is associated with thetopic “patents”).

A determination may be made at 333 whether third party metadata isassociated with the message. For example, third party metadata mayprovide additional context regarding the message or the user that isspecific to a company, team, channel, and/or the like. If there is thirdparty metadata associated with the message, such metadata may bedetermined at 337. In one implementation, the message may be parsed(e.g., using PHP commands) to determine third party metadata. Forexample, third party metadata may indicate whether the user who sent themessage is an authorized representative of the channel (e.g., anauthorized representative may be authorized by the company to respond toquestions in the channel).

A conversation primitive associated with the message may be determinedat 341. In one implementation, a conversation primitive is an elementused to analyze, index, store, and/or the like messages. For example,the message may be analyzed by itself, and may form its own conversationprimitive. In another example, the message may be analyzed along withother messages that make up a conversation, and the messages that makeup the conversation may form a conversation primitive. In oneimplementation, the conversation primitive may be determined as themessage, a specified number (e.g., two) of preceding messages and aspecified number (e.g., two) of following messages. In anotherimplementation, the conversation primitive may be determined based onanalysis of topics discussed in the message and other messages (e.g., inthe channel) and/or proximity (e.g., message send order proximity,message send time proximity) of these messages.

Message indexing of the message may be facilitated at 345. For example,various metadata, determined as described above, and/or the contents ofthe message may be used to index the message (e.g., using theconversation primitive) to facilitate various facets of searching. Inone implementation, a storage MSM message may be sent from MSM server206 to message indexing (e.g., SOLR) 210 to facilitate indexing. Inanother implementation, metadata associated with the message may bedetermined and the message may be indexed by message indexing 210. Inone embodiment, the message may be indexed such that a company's or ateam's messages are indexed separately (e.g., in a separate indexassociated with the team and/or company that is not shared with otherteams and/or companies). In one implementation, messages may be indexedat a separate distributed machine (e.g., to facilitate data isolationfor security purposes).

A determination may be made at 349 whether attachments are included withthe message. If there are attachments, file indexing of the filesassociated with the message may be facilitated at 353. For example, filecontents of the associated files (e.g., attached files, linked webpages,files from third part providers) may be used to index such files tofacilitate searching. In one implementation, the associated files may besent to file indexing (e.g., SOLR) 214. In one embodiment, the files maybe indexed such that a company's or a team's files are indexed at aseparate distributed machine.

The message may be stored at 357. In one implementation, a storage MSMmessage may be sent from MSM server 206 to MSM message database 218 tofacilitate message storage. For example, the stored message may beutilized for further analysis by other components of the MSM.

FIG. 4 shows a datagraph diagram illustrating embodiments of a workgraph and ML structure generating data flow for the MSM. In FIG. 4, aMSM message database 402 may send storage MSM messages 421 to a MSM datawarehouse 406. For example, the MSM message database may periodically(e.g., nightly, hourly) forward newly received storage MSM messages tothe MSM data warehouse for analysis.

A work graph generating (WGG) component 425 may utilize MSM messages(e.g., newly received MSM messages and/or MSM messages already stored inthe MSM data warehouse) to generate a work graph (e.g., for a teamassociated with the MSM messages). In one embodiment, a work graph maycapture relationships between users, between users and channels, betweenusers and topics, between channels and topics, between channels, betweentopics, and/or the like (e.g., relationships that are specific to theteam). See FIG. 5 for additional details regarding the WGG component.

A ML structure generating (MLSG) component 429 may utilize MSM messages,work graph data, other ML structure input data, and/or the like togenerate ML structure input data (e.g., team-level term priority), MLstructures (e.g., team-level neural networks), and/or the like tofacilitate ranking. See FIG. 7 for additional details regarding the MLSGcomponent.

The MSM data warehouse may send ML structure parameters 433 (e.g.,parameters that define a ML structure such as a neural network) to a MSMserver 410. For example, the MSM server may utilize the ML structure forranking (e.g., for the team). In one implementation, ML structureparameters may include data such as a ML structure identifier, MLstructure team identifier, ML structure application, ML structure type,ML structure inputs, ML structure output, ML structure parameters data(e.g., data that defines the neural network), and/or the like. Forexample, the MSM data warehouse may provide the following example MLstructure parameters, substantially in the form of a HTTP(S) POSTmessage including XML-formatted data, as provided below:

-   -   POST /ML_structure_parameters.php HTTP/1.1    -   Host: www.server.com    -   Content-Type: Application/XML    -   Content-Length: 667    -   <?XML version=“1.0” encoding=“UTF-8”?>    -   <ML_structure_parameters>        -   <ML structure            identifier>ID_ML_structure_1</ML_structure_identifier>        -   <ML_structure_team_identifier>ID_team_1</ML_structure_team_identifier>        -   <ML_structure_application>ranking of            messages</ML_structure_application>        -   <ML_structure_type>neural network</ML_structure_type>        -   <ML_structure_inputs>            -   user priority, user authority, channel priority, topic                priority, message text,            -   reactions, team-level term priority, team-level term                frequency, user's click through rate for channel        -   </ML_structure_inputs>        -   <ML_structure_output>message rank            score</ML_structure_output>        -   <ML_structure_parameters_data>binary data defining the ML            structure</ML_structure_parameters_data>    -   </ML_structure_parameters>

The MSM data warehouse may send (e.g., in XML format) ML structure inputdata 437 (e.g., work graph data) to message indexing 414. For example,the MSM server may obtain ML structure input data and utilize the MLstructure input data for ranking (e.g., for the team). In oneimplementation, ML structure input data may include data such as a MLstructure input identifier, ML structure input team identifier, MLstructure input name, ML structure input type, ML structure input data,and/or the like. For example, the MSM data warehouse may provide thefollowing example ML structure input data, substantially in the form ofa HTTP(S) POST message including XML-formatted data, as provided below:

POST /ML_structure_input_data.php HTTP/1.1 Host: www.server.comContent-Type: Application/XML Content-Length: 667 <?XML version = “1.0”encoding = “UTF-8”?> <ML_structure_input_data> <ML_structure_input_identifier>ID_ML_structure_input_1</ML_structure_input_identifier> <ML_structure_input_team_identifier>ID_team_1</ML_structure_input_team_identifier>  <ML_structure_input_name>userpriority</ML_ structure_input_name>  <ML_structure_input_data>   <user>   <user_identifier>ID_user_1</user_identifier>   <other_users_priorities>     <user>     <user_identifier>ID_user_2</user_identifier>     <priority>0.7</priority>     </user>     <user>     <user_identifier>ID_user_3</user_identifier>     <priority>0.3</priority>     </user>     ...   </other_users_priorities>   </user>   <user>   <user_identifier>ID_user_2</user_identifier>   <other_users_priorities>     <user>     <user_identifier>ID_user_1</user_identifier>     <priority>0.2</priority>     </user>     <user>     <user_identifier>ID_user_3</user_identifier>     <priority>0.5</priority>     </user>     ...   </other_users_priorities>   </user>   ...  </ML_structure_input_data></ML_structure_input_data>

FIG. 5 shows a logic flow diagram illustrating embodiments of a workgraph generating (WGG) component for the MSM. In FIG. 5, a work graph togenerate may be determined at 501. In one implementation, a teamidentifier may be provided that indicates a team for which to generatethe work graph. For example, work graphs may be generated periodicallyby a work graph generating process and the work graph generating processmay specify the team identifier. In another implementation, the workgraph may be generated for a company (e.g., the company may havemultiple teams).

MSM messages to use for work graph generation may be determined at 503.In one implementation, messages from channels accessible by the team maybe used (e.g., messages sent and/or received by members of the team,messages sent and/or received by anyone with access to the channel). Inanother implementation, messages from channels associated with thecompany may be used. In yet another implementation, direct messages(e.g., messages sent by users directly to each other instead of throughchannels) may also be used. In some embodiments, messages to use may befiltered, such as based on date (e.g., use messages sent and/or receivedwithin the last year).

A determination may be made at 505 whether there remain users toanalyze. In one implementation, any user on the team may be analyzed. Inanother implementation, any user in the company may be analyzed. Ifthere remain users to analyze, the next user to analyze may be selectedat 509.

The user's relationship to other users may be determined at 513. In oneimplementation, user to user data such as how many messages from anotheruser the user read, how many messages of another user the user reactedto, how many direct messages the user sent to another user, how manychannels the user and another user joined in common, and/or the like maybe used to calculate a user priority (e.g., a user priority score) ofanother user to the user. For example, a weighted average of user touser data may be calculated for each of the other users from theperspective of the user, and the resulting scores normalized so thateach of the other users is assigned a user priority score (e.g., in the0 to 1 range) from the perspective of the user. In another example, a MLtechnique (e.g., a neural network) may be used to calculate a userpriority score for each of the other users from the perspective of theuser.

The user's relationship to channels may be determined at 517. In oneimplementation, user to channel data such as whether the user joined achannel, how many messages the user sent in the channel, how manymessages the user read in the channel, how often the user checks thechannel, whether the user starred the channel, how similar the channelis to other channels the user participates in, and/or the like may beused to calculate a channel priority (e.g., a channel priority score) ofthe channel to the user. For example, a weighted average of user tochannel data may be calculated for each channel (e.g., each channelaccessible to the team, each channel accessible to the company), and theresulting scores normalized so that each of the channels is assigned achannel priority score (e.g., in the 0 to 1 range) from the perspectiveof the user. In another example, a ML technique (e.g., a neural network)may be used to calculate a channel priority score for each of thechannels from the perspective of the user.

The user's relationship to topics may be determined at 521. In oneimplementation, user to topic data such as how many messages the usersent regarding a topic, how many messages the user read regarding thetopic, how many reactions to the user's messages regarding the topichave been received, how many times files regarding the topic that wereattached to the user's messages have been downloaded by other users, howmany times files regarding the topic have been downloaded by the user,and/or the like may be used to calculate a topic priority (e.g., a topicpriority score) of the topic to the user. For example, a weightedaverage of user to topic data may be calculated for each topic (e.g.,each topic discussed by the team, each topic discussed at the company),and the resulting scores normalized so that each of the topics isassigned a topic priority score (e.g., in the 0 to 1 range) from theperspective of the user. In another example, a ML technique (e.g., aneural network) may be used to calculate a topic priority score for eachof the topics from the perspective of the user.

In another implementation, a statistical method (e.g., tf-idf, BM25) maybe used to calculate a topic expertise score of the user with regard toeach topic. For example, each user-channel pair (e.g., a user-channelpair may be thought of as a collection of messages (e.g., a document)associated with the user and a respective channel (e.g., sent by theuser in the respective channel)) may be ranked using BM25 (e.g., eachsuch document may be ranked) with regard to each topic to determine foreach respective topic whether the user is an expert who discussed therespective topic in the respective channel In some embodiments, theuser's messages may be weighted by recency. In some embodiments,synonyms and/or bigrams may be used when determining whether the userdiscussed a topic. In one implementation, the following technique may beused to calculate term frequency (tf):

Calculating tf case class TermFrequency(key: UserChannel, tf: Int,weightedTF: Double) tf: RDN(String, Seq[UserTermFrequency])]  For eachmessage:   Weight = 6 months / (6 months + message age)   Emit (word,(author-channel, weight)) tuples for each word in the message    Do notcount @-mentions as words    Use external ID to capture content inshared channels   Also emit (word, (mentionee-channel, weight)) tuplesfor each @-mention in the message   Reduce by key and create aTermFrequency per key   Sort the list by decreasing weightedTF, truncateto top 50  Also emit bigrams  Alterntively: index context messages too Alterntively: weight tf by some measure of importance   reply_count,reaction count Data cleaning & df df: RDD[(String, Double)] =tf.mapValues(_.size) totalTF: RDD[(String, Double)] =tf.mapValues(_.map(_.tf).sum)  Look at the distribution of words by   %of documents (users) with that term   Total occurrences of the term Discard very common terms (where df > 70%)  Discard very rare terms(where total term frequency is < 5)  Doc norms docNorm: RDD[(User,Double)] = 1 / Math.sqrt(# of tokens in this document)

A determination may be made at 531 whether there remain channels toanalyze. In one implementation, any channel accessible to the team maybe analyzed. In another implementation, any channel accessible to thecompany may be analyzed. If there remain channels to analyze, the nextchannel to analyze may be selected at 535.

The channel's relationship to other channels may be determined at 539.In one implementation, channel to channel data such as how many usersthe channel and another channel have in common, similarity of topicsdiscussed in the channel and another channel (e.g., based on channeldescriptions of these channels, based on topics of messages sent inthese channels), and/or the like may be used to calculate a channelsimilarity (e.g., a channel similarity score) of another channel to thechannel. For example, a weighted average of channel to channel data maybe calculated for each of the other channels from the perspective of thechannel, and the resulting scores normalized so that each of the otherchannels is assigned a channel similarity score (e.g., in the 0 to 1range) from the perspective of the channel. In another example, a MLtechnique (e.g., a neural network) may be used to calculate a channelsimilarity score for each of the other channels from the perspective ofthe channel.

A determination may be made at 541 whether there remain topics toanalyze. In one implementation, any topic discussed by the team may beanalyzed. In another implementation, any topic discussed at the companymay be analyzed. If there remain topics to analyze, the next topic toanalyze may be selected at 545.

The topic's relationship to other topics may be determined at 549. Inone implementation, a topic similarity (e.g., a topic similarity score)of a topic to another topic may be calculated. For example, termfrequency-inverse document frequency (tf-idf) of each word may bedetermined (e.g., based on analysis of messages from channels accessibleby the team, based on analysis of messages from channels associated withthe company), and a topic similarity score may be calculated for each ofthe other topics from the perspective of the topic based on the numberof words that the topic and another topic have in common with each wordweighted by tf-idf. In another example, a ML technique (e.g., a neuralnetwork) may be used to calculate a topic similarity score for each ofthe other topics from the perspective of the topic.

The generated work graph may be stored at 553. In one implementation,the work graph may be stored via a MySQL database command similar to thefollowing:

-   -   INSERT INTO WorkGraphs (workGraphID, workGraphTeamID,        -   workGraphUserData, workGraphChanneIData, workGraphTopicData)    -   VALUES (ID_WorkGraph_1, ID_team_1,        -   data regarding users, data regarding channels, data            regarding topics);

FIG. 6 shows an exemplary work graph for the MSM. In FIG. 6, the workgraph example shows relationships between users 601, 603, 605 and 607,channels 611, 613 and 615, and topics 621, 623, 625 and 627. In thiswork graph example, each arrow indicates a priority relationship (e.g.,user priority, channel priority or topic priority) or a similarityrelationship (e.g., channel similarity or topic similarity), and thethickness of each arrow indicates the strength (e.g., in the 0 to 1range) of the relationship (e.g., the thicker the arrow the stronger therelationship). For example, channel priority score of channel 613 (e.g.,“engineering—general discussion”) is higher than channel priority scoreof channel 615 (e.g., “legal—general discussion”) to user 603 (e.g., theuser may be a software developer who joined channel 613 and did not joinchannel 615). In another example, topic priority score of topic 625(e.g., “searching”) is higher than topic priority score of topic 621(e.g., “patents”) to user 603 (e.g., the user may be a softwaredeveloper who deals with searching (e.g., the user posts and reads manymessages regarding searching) and who may be interested in filing apatent application (e.g., the user downloaded a patent policy fileattached to a message from the company's patent counsel)). In oneimplementation, the absence of an arrow may indicate that the strengthof the relationship is below a specified threshold (e.g., 0, below 0.1).

As shown in the work graph example, in one implementation, the strengthsof user priority relationships between users may be asymmetric. Forexample, user priority score of user 605 is higher to user 603 (e.g.,user 605 may be a more experienced software developer who answersquestions posted by user 603) than user priority sore of user 603 touser 605 (e.g., user 603 may rarely answer questions posted by user605). In another implementation, the strengths of user priorityrelationships may be symmetric.

As shown in the work graph example, in one implementation, the strengthsof similarity relationships may be symmetric. For example, channelsimilarity score between channels 613 and 615 may be the same from theperspective of both channels. In another example, topic similarity scorebetween topics 623 and 625 may be the same from the perspective of bothtopics. In another implementation, the strengths of similarityrelationships may be asymmetric.

In some embodiments, work graph data may include multiple dimensions ofrelationships (e.g., data other than priority relationships andsimilarity relationships) that may facilitate ranking in a variety ofapplications. For example, in addition to a topic priority score fortopic 625 (e.g., how important this topic is to the user), work graphdata for user 603 may include a user authority score for topic 625(e.g., how much of an expert the user is in this topic). In thisexample, a high topic priority score may indicate that when the usersearches through messages, messages regarding this topic are likely tobe relevant to the user (e.g., application—determining relevantmessages), while a high user authority score may indicate that whenother users search through messages on this topic, the user may berecommended as an expert who may be able to answer questions regardingthis topic (e.g., application—determining relevant people).

FIG. 7 shows a logic flow diagram illustrating embodiments of a MLstructure generating (MLSG) component for the MSM. In FIG. 7, a MLstructure to generate (e.g., a neural network) may be determined at 701.For example, ML approaches such as unsupervised learning, supervisedlearning, reinforcement learning, deep learning, and/or the like may beused to generate the ML structure (e.g., using a machine learningpackage such as Spark ML, TensorFlow, etc.). In one implementation, ateam identifier may be provided that indicates a team for which togenerate the ML structure. For example, ML structures may be generatedperiodically by a ML process and the ML process may specify the teamidentifier. In another implementation, the ML structure may be generatedfor a company (e.g., the company may have multiple teams). In oneembodiment, the ML structure may be used for ranking (e.g., for messageranking, people ranking, channel ranking, and/or the like). In anotherembodiment, the ML structure may be used to generate ML input data(e.g., team-level term priority) that may be used as input to another MLstructure (e.g., ML structure used for ranking).

Inputs for the ML structure may be determined at 705. In oneimplementation, the inputs for the ML structure may be selected fromdata such as message content, file content, message metadata, work graphdata, other generated ML structure input data (e.g., generated viaanother ML structure (e.g., team-level term priority), calculated via astatistical method such as tf-idf (e.g., team-level term frequency) orBM25), search term, and/or the like. For example, the inputs for the MLstructure may be predetermined and may be retrieved via a MySQL databasecommand similar to the following:

-   -   SELECT ML_StructureInputs    -   FROM ML_Structures    -   WHERE ML_StructureID=ID_ML_structure_1;

Output for the ML structure may be determined at 709. In oneimplementation, the output for the ML structure may be selected fromranking outputs (e.g., message rank, person rank, channel rank), MLinput data outputs, and/or the like. For example, the output for the MLstructure may be predetermined and may be retrieved via a MySQL databasecommand similar to the following:

-   -   SELECT ML_StructureOutput    -   FROM ML_Structures    -   WHERE ML_StructureID=ID_ML_structure_1;

Training, validation, test, and/or the like data may be determined at713. For example, such data may be used to train one or more candidateML structures, to compare performances and select the best performing MLstructure from the candidate ML structures, to test the predictivestrength of the best performing ML structure, and/or the like. In oneimplementation, data associated with the team that corresponds to theinputs and/or the output of the ML structure may be utilized to selecttraining, validation, test, and/or the like subsets of data. In anotherimplementation, data associated with the company that corresponds to theinputs and/or the output of the ML structure may be utilized to selecttraining, validation, test, and/or the like subsets of data. The MLstructure may be trained (e.g., training, selecting the best performingcandidate, testing) at 717 using the determined training, validation,test, and/or the like data.

A determination may be made at 721 whether results (e.g., predictivestrength) associated with the trained ML structure are acceptable (e.g.,is the predictive strength high enough). If the results are notacceptable, changes to the ML structure may be determined at 723. Forexample, the number and/or types of inputs, the ML approach, thetraining, validation, test, and/or the like datasets, and/or the likemay be changed, and the ML structure may be retrained to attain betterresults (e.g., higher predictive strength).

If the results are acceptable, ML structure parameters of the MLstructure may be stored at 725. For example, the ML structure parametersmay define neural network parameters (e.g., interconnections betweenneurons, weights of the interconnections, activation function forneurons) of a neural network trained by the MLSG component. In variousimplementations, the ML structure parameters may be stored as binarydata, XML, and/or the like. For example, the ML structure parameters maybe stored via a MySQL database command similar to the following:

-   -   UPDATE ML_Structures    -   SET ML_StructureParametersData=binary data defining the ML        structure    -   WHERE ML_StructureID=ID_ML_structure_1;

In one embodiment, the MLSG component may be utilized to generate ahighlights model. The highlights model may be utilized to assignimportance scores for a given user at a given time. For example, thehighlights model may be used for features such as in-channel highlights,cross channel briefings, and/or the like.

In one implementation, in order to measure importance (e.g., ofmessages), the highlights model may utilize engagements as a proxy forimportance. As such, a message is considered important if the user ishighly likely to engage with it. In other words:

-   -   Importance˜P(engagement|user, message, time)

In various implementations, one or more engagement types may be trackedand/or utilized for ranking by the highlights model. An exemplary set ofengagement types is shown in the table below:

Engagement Types Clicks Reactions Replies File Clicks External ClicksMentions Shares Stars Implicit Replies Pins Reads Selections

In one implementation, a ML structure (e.g., a logistic regressionclassifier) may be generated for each engagement type utilized forranking. This classifier may be utilized to approximate the probabilityof a user engaging with a given message at a given time for the givenengagement type. For example, for clicks, the classifier may be utilizedto approximate:

-   -   Output=P(click|user, message, time)

For each classifier, the runtime (e.g., during ranking—see the RDcomponent and FIG. 9) input is a (user, message, time) and the runtimeoutput is a score that should be correlated with the probability of anengagement.

At training time, the input is a list of (user, message, time) tupleswith label 1 if an engagement was logged and label 0 otherwise. Sincefor most engagements there are more negative examples than positiveexamples, the negative examples may be subsampled for performance and/orquality reasons.

In some implementations, to make sure the model generalizes well tofuture inputs, the (user, message, time) tuples are expanded into anumber of features that should be correlated with the probability ofengagements. As well as the input (user, message, time) tuple, thefeatures rely on other (e.g., external) data such as work graph data(e.g., through aggregate signals such as user priority, which models howimportant a user A is to another user B). For example, features may beproperties of the message content, message metadata, messageinteractions, and/or the like (e.g., some of which may be augmented withproperties of the work graph). The table below lists an exemplary set offeatures that may be used by the classifiers:

Name Description author_priority The user priority of the author withrespect to the user. user_engagement_ The number of reactions on themessage. count_log_reactions engagement_user_ The user priority of theusers who reacted to priority_reactions the message with respect to theuser. has_file Whether the message contains a file or not. message_typeThe type of the message (e.g. regular, file_share, etc.) bot_message,count_words The number of words contained in the message. . . . . . .

FIG. 8 shows a datagraph diagram illustrating embodiments of a rankingdata flow for the MSM. In FIG. 8, dashed lines indicate data flowelements that may be more likely to be optional. In FIG. 8, a client 802may send a ranking request 821 to a MSM server 806. For example, theranking request may be a search request that includes a search term(e.g., “patents”) specified by a user. In one implementation, theranking request may include data such as a request identifier, a useridentifier (e.g., to facilitate access control), a ranking type (e.g.,search for messages, people, channels; recap a channel), a rankingfilter (e.g., search results should include messages and exclude peopleand channels), ranking details (e.g., a search term for a searchrequest, a channel identifier for a recap request), and/or the like. Forexample, the client may provide the following example ranking request,substantially in the form of a HTTP(S) POST message includingXML-formatted data, as provided below:

POST/ranking_request.php HTTP/1.1

-   -   Host: www.server.com    -   Content-Type: Application/XML    -   Content-Length: 667    -   <?XML version=“1.0” encoding=“UTF-8”?>    -   <ranking_request>        -   <request_identifier>ID_request_1</request_identifier>        -   <user_identifier>ID_user_2</user_identifier>        -   <ranking_type>search</ranking_type>        -   <ranking_details>            -   <search_term>patents</search_term>        -   </ranking_details>    -   </ranking_request>

In an alternative embodiment, the ranking request may be generated bythe MSM server. For example, the ranking request may be a channelsuggestion request (e.g., suggesting channels to join, leave, star,and/or the like) periodically generated by the MSM server for the user.In another example, the ranking request may be a contextual search forkey phrases in a channel to augment the user's reading experience withrelevant messages and/or files.

The MSM server may send a ranking data request 825 to message indexing810. In one implementation, the ranking data request may specify facetsof searching for message indexing 810 to filter over (e.g., search term,team and/or company identifier associated with the user). In anotherimplementation, the ranking data request may specify ML structure inputdata to obtain for ML structure(s) utilized for ranking. For example,the MSM server may provide the following example ranking data request,substantially in the form of a HTTP(S) POST message includingXML-formatted data, as provided below:

-   -   POST/ranking_data_request.php HTTP/1.1    -   Host: www.server.com    -   Content-Type: Application/XML    -   Content-Length: 667    -   <?XML version=“1.0” encoding=“UTF-8”?>    -   <ranking_data_request>        -   <request_identifier>ID_request_2</request_identifier>        -   <relevant_messages_to_get>            -   <access_control>data accessible to ID_team_1 (e.g., team                of ID_user_2)<access_control>            -   <search_term>patents</search_term>        -   </relevant_messages_to_get>        -   <ML_structure_input_data_to_get>            -   ID_ML_structure_input_1, ID_ML_structure_input_2,        -   </ML_structure_input_data_to_get>    -   </ranking_data_request>

Message indexing 810 may send a ranking data response 829 to the MSMserver with the requested data (e.g., relevant messages, ML structureinput data).

The MSM server may send a ranking data request 833 to file indexing 814.In one implementation, the ranking data request may specify facets ofsearching for file indexing 814 to filter over (e.g., search term, teamand/or company identifier associated with the user). File indexing 814may send a ranking data response 837 to the MSM server with therequested data (e.g., relevant files).

A ranking determining (RD) component 841 may utilize ranking data (e.g.,relevant messages, relevant files, ML structure input data) to rank therelevant messages and/or files, people, channels, and/or the like usingML structure(s). See FIG. 9 for additional details regarding the RDcomponent.

The MSM server may send a ranking response 845 to the client. Theranking response may be used to provide the highest ranked messages,files, people, channels, and/or the like to the client. For example, theclient may utilize the ranking response to display results (e.g., searchresults, a recap of a channel, channel suggestions) to the user. SeeFIGS. 12-16 for examples of results that may be displayed to the user.

FIG. 9 shows a logic flow diagram illustrating embodiments of a rankingdetermining (RD) component for the MSM. In FIG. 9, a ranking request maybe obtained at 901. For example, the ranking request may be obtained asa result of a user executing a search. In another example, the rankingrequest may be obtained as a result of a ranking process periodicallygenerating channel suggestions for the user. In some implementations,ranking requests may exclude specific queries (e.g., exclude searchterms in a search), may exclude users (e.g., exclude the user), mayexclude contexts (e.g., exclude specified channels), and/or the like. Insome implementations, multiple ranking requests may be generated (e.g.,for a search) to generate different types of results. For example, whenthe user executes the search, a ranking request may be generated todetermine relevant messages, another ranking request may be generated todetermine relevant people, and another ranking request may be generatedto determine relevant channels. In another example, when the userexecutes the search, multiple ranking requests may be generated toutilize multiple ML structures (e.g., classifiers) to rank results(e.g., messages) and scores from these classifiers may be combined intoa single score using a combiner heuristic. In some implementations, a MLstructure may be utilized to determine which ranking requests should begenerated (e.g., is the user looking for a specific message, an expert,a channel in which to ask a question, or an answer to a question).

The ranking type of the ranking request may be determined at 905. In oneembodiment, the ranking type may indicate the application associatedwith the ranking request. For example, such applications may includedetermining relevant messages, conversations, files, people, channels,and/or the like in response to the user's search query; generating arecap of a channel for the user; suggesting channels for the user tojoin, leave, star, and/or the like; providing a push notification of aspecified number of the most important messages for the user for theday. In another embodiment, the ranking type may indicate whichclassifier to utilize for ranking (e.g., based on the engagement type).In one implementation, the ranking request may be parsed (e.g., usingPHP commands) to determine the ranking type.

The applicable ML structure (e.g., neural network) to use for theranking request may be retrieved at 909. In one embodiment, different MLstructures may be used for different types of ranking requests and/orfor different teams and/or companies. In one implementation, theapplicable ML structure may be retrieved based on the ranking type andthe team identifier associated with the user (e.g., the team identifierof the team to which the user is assigned). For example, the applicableML structure may be retrieved via a MySQL database command similar tothe following:

-   -   SELECT *    -   FROM ML_Structures    -   WHERE ML_StructureApplication=search_messages AND        ML_StructureTeamID=ID_team_1;

ML structure inputs for the retrieved ML structure may be determined at913. In one implementation, the ML structure inputs may utilize datasuch as message content, file content, message metadata, work graphdata, other generated ML structure input data (e.g., generated viaanother ML structure (e.g., team-level term priority), calculated via astatistical method such as tf-idf (e.g., team-level term frequency) orBM25), search term, and/or the like. For example, the ML structureinputs may be determined via a MySQL database command similar to thefollowing:

-   -   SELECT ML_StructureInputs    -   FROM ML_Structures    -   WHERE ML_StructureID=ID_ML_structure_1;

Ranking data for the determined ML structure inputs may be obtained at917. In one embodiment, the ranking data may include relevant messages,files, people, channels, and/or the like. For example, messagesaccessible to the user (e.g., messages associated with the teamidentifier associated with the user) may be searched for a search termspecified by the user, and matching messages may be obtained. In anotherembodiment, the ranking data may include generated ML structure inputdata. For example, generated ML structure input data accessible to theuser (e.g., generated ML structure input data associated with the teamidentifier associated with the user) that correspond to the ML structureinputs may be obtained. In one implementation, a ranking data requestmay be sent to obtain the ranking data.

A determination may be made at 921 whether there remain applicable datato rank. In one implementation, any relevant messages, files, people,channels, and/or the like may be ranked. If there remain applicable datato rank, the next applicable data item may be selected at 925.

Values for ML structure inputs for the selected applicable data item maybe determined at 929. In one implementation, values for the ML structureinputs may be determined based on message metadata. For example, ifchannel priority for the user is one of the ML structure inputs,metadata associated with the selected message may be analyzed todetermine a channel identifier associated with the selected message. Thechannel identifier may be used in a second ML structure (e.g., a MLstructure that determines channel priority for the user) to determinechannel priority of the channel where with the selected message was sentfor the user. In another implementation, values for ML structure inputsmay be determined based on work graph and/or other generated MLstructure input data. For example, if user priority for the user is oneof the ML structure inputs, work graph data associated with the user'steam may be analyzed to determine user priority of the user who sent theselected message to the user executing the search.

A ranking score for the selected applicable data item may be determinedat 933. In one implementation, the determined values for ML structureinputs may be provided to the applicable ML structure, which maygenerate the ranking score for the selected applicable data item. Forexample, a message rank score may indicate how relevant the selectedapplicable data item is with regard to the ranking request (e.g., howrelevant the selected message is with regard to the user's search).

In some alternative embodiments, statistical methods may be used to rankapplicable data items (e.g., instead of a ML structure). For example, asearch for experts (e.g., triggered based on a heuristic or a MLclassifier) may utilize tf-idf, BM25, and/or the like techniques to rankusers as experts with regard to a given topic (e.g., specified in theuser's search query). In one implementation, the following technique maybe used:

Calculating tf-idf for a given query  Let N = total number of documents(e.g., user-channel pairs)  For each word in the query:   For eachuser-channel in tf(word), calculate tf-idf score:    If =pow(userTF.weightedTF, 0.75)    idf = Math.log(N /(1 + df(word))    norm= docNorm(user)    tfidf = tf * idf * idf * norm  Group scores byuser-channel key and sum  Return top 5 user-channels  Alternatively: UseBM25 for scoring  Alternatively: Use channels instead of user-channelpairs  Alternatively: Allow experts to not match all terms in the query,using the “2<75%” rule. That is, if the query has 1 or 2 terms, they arerequired. If the query has 3 terms, 2 are required; 4 terms, 3 arerequired, etc.

Highest ranked applicable data items to use may be determined at 937. Inone implementation, a threshold number of highest ranked applicable dataitems may be specified (e.g., use up to 10 most relevant messages). Inanother implementation, a threshold ranking score may be specified(e.g., use messages with message rank score of at least 0.7 out of 1).

A ranking response may be provided at 941. In one implementation, theranking response may be used to provide the highest ranked applicabledata items to the user. For example, search results that include thehighest ranked messages may be displayed to the user. In anotherexample, a recommendation for channels to join, leave, star, and/or thelike may be displayed to the user.

In one embodiment, the RD component may be utilized for ranking usingthe highlights model (e.g., see the MLSG component and FIG. 7). In oneimplementation, the highlights model may combine the output of eachclassifier into a single score using a combiner heuristic (e.g., thegeneralized mean of the engagement types used for ranking). For example,if click, reaction, and reply engagement types are utilized for ranking,using the aliases P_(type) for each output, the combiner heuristic maybe:Score=((P _(click) ¹² +P _(reaction) ¹² +P _(reply) ¹²)/3)^(1/2)

The score may be used for ranking (e.g., cross channel briefing),thresholding (e.g., in-channel highlights), and/or the likeapplications.

In some implementations, one or more post processing heuristics may beapplied after scoring. For example, the post processing heuristics mayinclude:

Feedback

Some of the highlights products include a feedback mechanism that allowsthe user to provide feedback on a message. As part of this mechanism,the user can request to receive fewer messages from a given user,channel or containing links from a particular domain. This feedback maybe used to scale the scores down for messages that match negativefeedback received in the past from the user.

Justifications

The scores for each message may be justified post ranking using a numberof templates. For example, a message with reactions from users with highuser priority with respect to the current user might be justified asimportant using the string: “@user1, @user2 and @user3 reacted to . . .”. Message for which no template fits may be discarded.

Diversity

Some of the highlights products include a diversity heuristic. In thecase of cross channel briefing for example, no more than N messages arekept from the same user, channel or file type. In the case of in-channelhighlights, the top scoring message from a run of consecutive highscoring messages may be kept.

Thresholding

For some highlights products, messages may be thresholded by score andin number. For example, for cross channel briefings, messages above acertain score may be considered and the top 10 messages with a highenough score may be kept.

Small Messages

Small messages, containing a few words or emojis, may be discarded.

Unfurls

Messages containing links where the link unfurls into a separate messagemay be grouped together and counted as one.

Time Decay

For some highlights products such as cross-channel briefing, a timedecay may be applied to the score for each message to boost more recentmessages and downweight older messages.

FIG. 10 shows a screenshot diagram illustrating embodiments of the MSM.In FIG. 10, screen 1001 shows that a user may join one or more teams. Inone embodiment, a team may be public (e.g., any company employee mayjoin the team). For example, the user may join Garlic Crouton team 1003.In another embodiment, a team may be private (e.g., the team may berestricted to certain company employees) For example, the user mayrequest access to Lemon Croutons team 1005, and, if the user meetscriteria for joining the team, the user may be allowed to join the team.In one implementation, a ranking request may be used to determine teamsto recommend for the user to join (e.g., utilizing the user's data suchas the user's job title, geographic location, and/or the like). The usermay also browse 1007 teams in the user's company that the user may join.

Screen 1010 shows various teams in a company, and a user's membershipstatus with regard to these teams. For example, the user may be a memberof two out of five teams. In one embodiment, when the user sends aranking request, messages associated with these two teams may beanalyzed.

FIG. 11 shows a screenshot diagram illustrating embodiments of the MSM.In FIG. 11, screen 1101 illustrates global channels. In one embodiment,a member of any of the teams (e.g., anyone in the company) may findand/or join global channels. For example, members of both Accounts teamand Mobile team may join the “food-n-drink, SF” channel.

Screen 1110 illustrates select team channels. In one embodiment, access(e.g., ability to find and/or join) to select team channels may berestricted to members of specified teams. For example, members ofAccounts team have access to “accounts-billing” channel, but members ofMobile team do not have access to this channel.

FIG. 12 shows a screenshot diagram illustrating embodiments of the MSM.In FIG. 12, screen 1201 illustrates search results of a search conductedfor a user. As shown at 1205, the user specified “joint marketing” asthe search term for the search. In one implementation, the searchresults may be displayed to the user sorted based on relevance 1207(e.g., highest ranked applicable data items with higher ranking scoresare shown first). In another implementation, the search results may bedisplayed to the user sorted based on other criteria (e.g., most recenthighest ranked applicable data items are shown first).

The results of this search include two types of results, messages 1210and files 1215. In one implementation, the user may select which type ofresults to view. In another implementation, different result types maybe shown together in one view. As shown at 1220, the results aredetermined using data for the user's company. In some implementations,the user may choose to obtain search results using data for one or moreuser selected teams associated with the user.

Message 1225 is shown as the highest ranked search result for thesearch. The message may be one of the messages in a conversationprimitive partially shown at 1230. The user may view the other messagesin the conversation primitive using the “Expand” GUI widget.

FIG. 13 shows a screenshot diagram illustrating embodiments of the MSM.In FIG. 13, screen 1301 illustrates search results of a search conductedfor a user. As shown at 1305, the user specified “slacklab” as thesearch term for the search. In addition to messages and files, theresults of the search include people. As shown at 1310, three teammembers (e.g., with the highest ranking scores) are included in thesearch results. For example, a person may be thought of a collection ofmessages associated with the person, and may be ranked using dataregarding this collection of messages, work graph data, and/or the like.

FIG. 14 shows a screenshot diagram illustrating embodiments of the MSM.In FIG. 14, screen 1401 illustrates that a user may be shown a channelrecap notification. For example, the user may not have been able to readmessages a channel for some time, and the channel recap notification maybe provided to help the user catch up with the channel. The channelrecap notification may inform the user regarding important messages(e.g., 6 important messages) sent in the channel while the user wasaway. In one implementation, the user may click on the channel recapnotification to view a channel recap.

Screen 1410 illustrates the channel recap that may be displayed for theuser. In one implementation, the important messages may be marked withdots 1413, 1415, 1417 on the scrollbar to facilitate finding thesemessages. For example, the important messages may be messages with thehighest ranking scores sent in the channel while the user was away.

Screen 1420 illustrates that the user may utilize a GUI widget to view arecap of the channel at any time. In one implementation, the channelrecap may be configured to display the most important messages for aspecified time period (e.g., the most important messages sent in thelast week).

Screen 1430 illustrates that the user may select how a channel recapshould be configured. For example, if the user joins a new channel thatalready has a lot of messages, the user may wish to catch up on the 20most important messages sent in this channel in the last six month.

FIG. 15 shows a screenshot diagram illustrating embodiments of the MSM.In FIG. 15, screen 1501 illustrates how various channels may be sortedand/or prioritized. As shown at 1505 a user may choose to prioritizechannels with mentions and highlight words.

Screen 1510 illustrates ranking scores (e.g., prediction column) forvarious channels (e.g., channel column) for the user (e.g., usernamecolumn). For example, the user's channel priority preferences and dataregarding the various channels (e.g., whether the user starred achannel, data regarding messages sent in a channel, work graph data) maybe used to determine ranking scores for the various channels for theuser.

FIG. 16 shows a screenshot diagram illustrating embodiments of the MSM.As shown in FIG. 16, ranking scores for channels may be utilized for avariety of applications. Screen 1601 illustrates how channels may besorted for a user. As shown, channels starred by the user may be shownon top in Starred section, priority channels (e.g., channels with thehighest ranking scores) may be determined and/or sorted (e.g., channelswith higher ranking scores are shown first) and shown below in Prioritysection, and other channels may be shown below in Channels section.

Screen 1605 illustrates suggestions of channels to join, leave, star,and/or the like that may be generated for the user (e.g., based on dataregarding the user). For example, three channels to join may besuggested to the user. Screen 1610 illustrates channel suggestions thatmay be provided to the user when a user joins a channel (e.g., based oninterests of other people in the channel and data regarding the user).For example, three channels to join may be suggested to the user.

FIGS. 17A-17C show screenshot diagrams illustrating embodiments of theMSM. In FIG. 17, screen 1701 illustrates that a user may be shown acollapsed results teaser to inform the user that experts for a userspecified search term (e.g., solr) have been found (e.g., based on topicexpertise scores for topic solr). In one implementation, the user mayclick on the results teaser to view results.

Screen 1710 illustrates different types of results that may be shown tothe user (e.g., ordered based on ranking from highest (1) to lowest(3)). In one implementation, the topic (e.g., solr) may have beendiscussed by an expert in a channel. Different result configurations areillustrated for cases where (1) a single expert discussed the topic in asingle channel, (1a) a single expert discussed the topic in multiplechannels, and (1b) multiple experts discussed the topic in a singlechannel. For example, clicking on an expert may take the user to therelevant channel, where the user may ask a question and discuss theanswer. In another implementation, the topic may have been discussed ina channel. Different result configurations are illustrated for caseswhere (2) a channel has a channel purpose description, (2a) a channeldoes not have a channel purpose description but has other discussedtopics, and (2b) a channel does not have a channel purpose descriptionor other discussed topics. In yet another implementation, the topic mayhave been discussed by an expert but not in a particular channel (e.g.,in direct messages to other users). A result configuration (3) isillustrated for this case.

Screen 1720 illustrates an example of results that may be shown to theuser. Screen 1730 illustrates various actions that may be taken inresponse to the user clicking on various elements of the results GUI.

FIG. 18 shows a screenshot diagram illustrating embodiments of the MSM.In FIG. 18, screen 1801 illustrates a cross-channel briefing ofmessages. In one embodiment, the briefing may be a GUI section with aset of diverse, interesting, unread messages. For example, the briefingmay help people catch up quickly on important unread messages acrossjoined channels by grouping them into a single dedicated section. In oneimplementation, in order to determine messages to show in the briefing,a multi-pass ranking approach using starred channel and channel prioritytiers may be used as follows:

-   -   Tier 1: Take the top X % of starred channels based on channel        priority. Select the highest scoring messages, and order the        channels by score.    -   Tier 2: Take the bottom X % of starred channels based on channel        priority. Select the highest scoring messages, and order the        channels by score.    -   Tier 3: Take your top remaining channels by the top X % of        priority (e.g., 20%, 30%), and do the same pass.    -   Tier 4: If there's still room left in the briefing, select the        most important messages from the remaining channels.    -   Regardless of score, direct mentions may be included in the        briefing unless the briefing has already been filled with        messages in higher tiers.

Screen 1810 illustrates a feedback mechanism that may be utilized by auser to provide feedback regarding a message included in the briefing.Screen 1820 illustrates that when the user chooses to give positive ornegative feedback, a second set of optional choices may be shown toobtain additional feedback (e.g., to help improve a ML model). Forexample, positive feedback choices may include: show me more messageslike this, show me more from this channel, show me more from thisperson, show me more from this <domain>. In another example, negativefeedback choices may include: this message isn't helpful, show me lessfrom this channel, show me less from this person, show me less from this<domain>. In one implementation, a default optional choice may be usedif the user does not select an optional choice.

FIG. 19 shows a screenshot diagram illustrating alternative embodimentsof the MSM.

1. Instead of headings for each channel: add a star to the left so wecan star directly from the Briefing

-   -   1a. If you give positive feedback on a message that's not a        starred channel, we can show a coachmark

2. Sectionize Briefings between starred/unstarred section: use an arrowto show how you can move things up

3. Make star channel one of the feedback options

4. Replace TS feedback link with random tip generator and learn-morelinks

5. Clickable justifications

FIG. 20 shows a screenshot diagram illustrating alternative embodimentsof the MSM.

1. Show a series of messages of 1 by 1 and ask whether each wasimportant. Show the justification for why it was shown. Show people howto give feedback.

2. Use coachmarks in various places.

3. Show “we think these are your 5 most important channels” in aninterstitial and ask if you agree.

4. Give people all unreads and ask them to curate a briefing the firsttime to capture the intent of what Briefings is

FIG. 21 shows a screenshot diagram illustrating alternative embodimentsof the MSM.

1. A “Just the Highlights” walkthrough with next buttons to help you gothrough it

2. On first-use, just show one message with big callouts on what's goingon

3. Add a tip underneath each Briefing message that's related to thejustification

4. Explanation below each specific message “why am I seeing this?”

5. Show a count of feedback you've given so it communicates that we'relistening.

FIG. 22 shows a screenshot diagram illustrating alternative embodimentsof the MSM.

2. Refresh button (or pull-down on Mobile)

3. Briefings history goes to a different place

4. Add “starring” terminology to justifications

5. Add a dark shade into All Unreads where the thing you can see isBriefings

FIG. 23 shows a screenshot diagram illustrating alternative embodimentsof the MSM.

1. Each message we show is specifically to teach one action. Highlightthe important action.

2. Use Slackbot as the place to store SLI stuff. Use SLI to play a gameto have fun and give us information at the same time.

3. Show a “before” Briefing without any curation and an “after” Briefingafter you've done fun things

6. Build a fake demo Briefing using universal pop culture so peopleunderstand why things are important/personalized

7. Carousel of hints and tips

FIG. 24 shows a screenshot diagram illustrating alternative embodimentsof the MSM.

1. Warm welcome that highlights parts of the UI and darkens the rest inlogical order.

2-6: Coachmark various places with relevant text.

5. “Tell us how we did” coachmark that points to feedback controls

6. Point a coachmark at the sidebar

7. Clippy! Wizard!

8. Shake 8-ball for random tips

FIG. 25 shows a screenshot diagram illustrating alternative embodimentsof the MSM.

1. Coachmarks

2. Clippy!

3. Badge the All Unreads when we think there's a Briefing that's reallygood. Don't enable Briefings until we know they have a really good one.

4. Hide the first very good Briefing behind an unlock button

5. Make the unlock button a cool treasure chest.

MSM Controller

FIG. 26 shows a block diagram illustrating embodiments of a MSMcontroller. In this embodiment, the MSM controller 2601 may serve toaggregate, process, store, search, serve, identify, instruct, generate,match, and/or facilitate interactions with a computer through internetmessaging technologies, and/or other related data.

Typically, users, which may be people and/or other systems, may engageinformation technology systems (e.g., computers) to facilitateinformation processing. In turn, computers employ processors to processinformation; such processors 2603 may be referred to as centralprocessing units (CPU). One form of processor is referred to as amicroprocessor. CPUs use communicative circuits to pass binary encodedsignals acting as instructions to enable various operations. Theseinstructions may be operational and/or data instructions containingand/or referencing other instructions and data in various processoraccessible and operable areas of memory 2629 (e.g., registers, cachememory, random access memory, etc.). Such communicative instructions maybe stored and/or transmitted in batches (e.g., batches of instructions)as programs and/or data components to facilitate desired operations.These stored instruction codes, e.g., programs, may engage the CPUcircuit components and other motherboard and/or system components toperform desired operations. One type of program is a computer operatingsystem, which, may be executed by CPU on a computer; the operatingsystem enables and facilitates users to access and operate computerinformation technology and resources. Some resources that may beemployed in information technology systems include: input and outputmechanisms through which data may pass into and out of a computer;memory storage into which data may be saved; and processors by whichinformation may be processed. These information technology systems maybe used to collect data for later retrieval, analysis, and manipulation,which may be facilitated through a database program. These informationtechnology systems provide interfaces that allow users to access andoperate various system components.

In one embodiment, the MSM controller 2601 may be connected to and/orcommunicate with entities such as, but not limited to: one or more usersfrom peripheral devices 2612 (e.g., user input devices 2611); anoptional cryptographic processor device 2628; and/or a communicationsnetwork 2613.

Networks are commonly thought to comprise the interconnection andinteroperation of clients, servers, and intermediary nodes in a graphtopology. It should be noted that the term “server” as used throughoutthis application refers generally to a computer, other device, program,or combination thereof that processes and responds to the requests ofremote users across a communications network. Servers serve theirinformation to requesting “clients.” The term “client” as used hereinrefers generally to a computer, program, other device, user and/orcombination thereof that is capable of processing and making requestsand obtaining and processing any responses from servers across acommunications network. A computer, other device, program, orcombination thereof that facilitates, processes information andrequests, and/or furthers the passage of information from a source userto a destination user is commonly referred to as a “node.” Networks aregenerally thought to facilitate the transfer of information from sourcepoints to destinations. A node specifically tasked with furthering thepassage of information from a source to a destination is commonly calleda “router.” There are many forms of networks such as Local Area Networks(LANs), Pico networks, Wide Area Networks (WANs), Wireless Networks(WLANs), etc. For example, the Internet is generally accepted as beingan interconnection of a multitude of networks whereby remote clients andservers may access and interoperate with one another.

The MSM controller 2601 may be based on computer systems that maycomprise, but are not limited to, components such as: a computersystemization 2602 connected to memory 2629.

Computer Systemization

A computer systemization 2602 may comprise a clock 2630, centralprocessing unit (“CPU(s)” and/or “processor(s)” (these terms are usedinterchangeable throughout the disclosure unless noted to the contrary))2603, a memory 2629 (e.g., a read only memory (ROM) 2606, a randomaccess memory (RAM) 2605, etc.), and/or an interface bus 2607, and mostfrequently, although not necessarily, are all interconnected and/orcommunicating through a system bus 2604 on one or more (mother)board(s)2602 having conductive and/or otherwise transportive circuit pathwaysthrough which instructions (e.g., binary encoded signals) may travel toeffectuate communications, operations, storage, etc. The computersystemization may be connected to a power source 2686; e.g., optionallythe power source may be internal. Optionally, a cryptographic processor2626 may be connected to the system bus. In another embodiment, thecryptographic processor, transceivers (e.g., ICs) 2674, and/or sensorarray (e.g., accelerometer, altimeter, ambient light, barometer, globalpositioning system (GPS) (thereby allowing MSM controller to determineits location), gyroscope, magnetometer, pedometer, proximity,ultra-violet sensor, etc.) 2673 may be connected as either internaland/or external peripheral devices 2612 via the interface bus I/O 2608(not pictured) and/or directly via the interface bus 2607. In turn, thetransceivers may be connected to antenna(s) 2675, thereby effectuatingwireless transmission and reception of various communication and/orsensor protocols; for example the antenna(s) may connect to varioustransceiver chipsets (depending on deployment needs), including:Broadcom BCM4329FKUBG transceiver chip (e.g., providing 802.11n,Bluetooth 2.1+EDR, FM, etc.); a Broadcom BCM4752 GPS receiver withaccelerometer, altimeter, GPS, gyroscope, magnetometer; a BroadcomBCM4335 transceiver chip (e.g., providing 2G, 3G, and 4G long-termevolution (LTE) cellular communications; 802.11ac, Bluetooth 4.0 lowenergy (LE) (e.g., beacon features)); a Broadcom BCM43341 transceiverchip (e.g., providing 2G, 3G and 4G LTE cellular communications; 802.11g/, Bluetooth 4.0, near field communication (NFC), FM radio); anInfineon Technologies X-Gold 618-PMB9800 transceiver chip (e.g.,providing 2G/3G HSDPA/HSUPA communications); a MediaTek MT6620transceiver chip (e.g., providing 802.11a/ac/b/g/n, Bluetooth 4.0 LE,FM, GPS; a Lapis Semiconductor ML8511 UV sensor; a maxim integratedMAX44000 ambient light and infrared proximity sensor; a TexasInstruments WiLink WL1283 transceiver chip (e.g., providing 802.11n,Bluetooth 3.0, FM, GPS); and/or the like. The system clock typically hasa crystal oscillator and generates a base signal through the computersystemization's circuit pathways. The clock is typically coupled to thesystem bus and various clock multipliers that will increase or decreasethe base operating frequency for other components interconnected in thecomputer systemization. The clock and various components in a computersystemization drive signals embodying information throughout the system.Such transmission and reception of instructions embodying informationthroughout a computer systemization may be commonly referred to ascommunications. These communicative instructions may further betransmitted, received, and the cause of return and/or replycommunications beyond the instant computer systemization to:communications networks, input devices, other computer systemizations,peripheral devices, and/or the like. It should be understood that inalternative embodiments, any of the above components may be connecteddirectly to one another, connected to the CPU, and/or organized innumerous variations employed as exemplified by various computer systems.

The CPU comprises at least one high-speed data processor adequate toexecute program components for executing user and/or system-generatedrequests. The CPU is often packaged in a number of formats varying fromlarge supercomputer(s) and mainframe(s) computers, down to minicomputers, servers, desktop computers, laptops, thin clients (e.g.,Chromebooks), netbooks, tablets (e.g., Android, iPads, and Windowstablets, etc.), mobile smartphones (e.g., Android, iPhones, Nokia, Palmand Windows phones, etc.), wearable device(s) (e.g., watches, glasses,goggles (e.g., Google Glass), etc.), and/or the like. Often, theprocessors themselves will incorporate various specialized processingunits, such as, but not limited to: integrated system (bus) controllers,memory management control units, floating point units, and evenspecialized processing sub-units like graphics processing units, digitalsignal processing units, and/or the like. Additionally, processors mayinclude internal fast access addressable memory, and be capable ofmapping and addressing memory 2629 beyond the processor itself; internalmemory may include, but is not limited to: fast registers, variouslevels of cache memory (e.g., level 1, 2, 3, etc.), RAM, etc. Theprocessor may access this memory through the use of a memory addressspace that is accessible via instruction address, which the processorcan construct and decode allowing it to access a circuit path to aspecific memory address space having a memory state. The CPU may be amicroprocessor such as: AMD's Athlon, Duron and/or Opteron; Apple's Aseries of processors (e.g., A5, A6, A7, A8, etc.); ARM's application,embedded and secure processors; IBM and/or Motorola's DragonBall andPowerPC; IBM's and Sony's Cell processor; Intel's 80X86 series (e.g.,80386, 80486), Pentium, Celeron, Core (2) Duo, i series (e.g., i3, i5,i7, etc.), Itanium, Xeon, and/or XScale; Motorola's 680X0 series (e.g.,68020, 68030, 68040, etc.); and/or the like processor(s). The CPUinteracts with memory through instruction passing through conductiveand/or transportive conduits (e.g., (printed) electronic and/or opticcircuits) to execute stored instructions (i.e., program code) accordingto conventional data processing techniques. Such instruction passingfacilitates communication within the MSM controller and beyond throughvarious interfaces. Should processing requirements dictate a greateramount speed and/or capacity, distributed processors (e.g., seeDistributed MSM below), mainframe, multi-core, parallel, and/orsuper-computer architectures may similarly be employed. Alternatively,should deployment requirements dictate greater portability, smallermobile devices (e.g., Personal Digital Assistants (PDAs)) may beemployed.

Depending on the particular implementation, features of the MSM may beachieved by implementing a microcontroller such as CAST's R8051XC2microcontroller; Intel's MCS 51 (i.e., 8051 microcontroller); and/or thelike. Also, to implement certain features of the MSM, some featureimplementations may rely on embedded components, such as:Application-Specific Integrated Circuit (“ASIC”), Digital SignalProcessing (“DSP”), Field Programmable Gate Array (“FPGA”), and/or thelike embedded technology. For example, any of the MSM componentcollection (distributed or otherwise) and/or features may be implementedvia the microprocessor and/or via embedded components; e.g., via ASIC,coprocessor, DSP, FPGA, and/or the like. Alternately, someimplementations of the MSM may be implemented with embedded componentsthat are configured and used to achieve a variety of features or signalprocessing.

Depending on the particular implementation, the embedded components mayinclude software solutions, hardware solutions, and/or some combinationof both hardware/software solutions. For example, MSM features discussedherein may be achieved through implementing FPGAs, which are asemiconductor devices containing programmable logic components called“logic blocks”, and programmable interconnects, such as the highperformance FPGA Virtex series and/or the low cost Spartan seriesmanufactured by Xilinx. Logic blocks and interconnects can be programmedby the customer or designer, after the FPGA is manufactured, toimplement any of the MSM features. A hierarchy of programmableinterconnects allow logic blocks to be interconnected as needed by theMSM system designer/administrator, somewhat like a one-chip programmablebreadboard. An FPGA's logic blocks can be programmed to perform theoperation of basic logic gates such as AND, and XOR, or more complexcombinational operators such as decoders or mathematical operations. Inmost FPGAs, the logic blocks also include memory elements, which may becircuit flip-flops or more complete blocks of memory. In somecircumstances, the MSM may be developed on regular FPGAs and thenmigrated into a fixed version that more resembles ASIC implementations.Alternate or coordinating implementations may migrate MSM controllerfeatures to a final ASIC instead of or in addition to FPGAs. Dependingon the implementation all of the aforementioned embedded components andmicroprocessors may be considered the “CPU” and/or “processor” for theMSM.

Power Source

The power source 2686 may be of any standard form for powering smallelectronic circuit board devices such as the following power cells:alkaline, lithium hydride, lithium ion, lithium polymer, nickel cadmium,solar cells, and/or the like. Other types of AC or DC power sources maybe used as well. In the case of solar cells, in one embodiment, the caseprovides an aperture through which the solar cell may capture photonicenergy. The power cell 2686 is connected to at least one of theinterconnected subsequent components of the MSM thereby providing anelectric current to all subsequent components. In one example, the powersource 2686 is connected to the system bus component 2604. In analternative embodiment, an outside power source 2686 is provided througha connection across the I/O 2608 interface. For example, a USB and/orIEEE 1394 connection carries both data and power across the connectionand is therefore a suitable source of power.

Interface Adapters

Interface bus(ses) 2607 may accept, connect, and/or communicate to anumber of interface adapters, conventionally although not necessarily inthe form of adapter cards, such as but not limited to: input outputinterfaces (I/O) 2608, storage interfaces 2609, network interfaces 2610,and/or the like. Optionally, cryptographic processor interfaces 2627similarly may be connected to the interface bus. The interface busprovides for the communications of interface adapters with one anotheras well as with other components of the computer systemization.Interface adapters are adapted for a compatible interface bus. Interfaceadapters conventionally connect to the interface bus via a slotarchitecture. Conventional slot architectures may be employed, such as,but not limited to: Accelerated Graphics Port (AGP), Card Bus,(Extended) Industry Standard Architecture ((E)ISA), Micro ChannelArchitecture (MCA), NuBus, Peripheral Component Interconnect (Extended)(PCI(X), PCI Express, Personal Computer Memory Card InternationalAssociation (PCMCIA), and/or the like.

Storage interfaces 2609 may accept, communicate, and/or connect to anumber of storage devices such as, but not limited to: storage devices2614, removable disc devices, and/or the like. Storage interfaces mayemploy connection protocols such as, but not limited to: (Ultra)(Serial) Advanced Technology Attachment (Packet Interface) ((Ultra)(Serial) ATA(PI)), (Enhanced) Integrated Drive Electronics ((E)IDE),Institute of Electrical and Electronics Engineers (IEEE) 1394, fiberchannel, Small Computer Systems Interface (SCSI), Universal Serial Bus(USB), and/or the like.

Network interfaces 2610 may accept, communicate, and/or connect to acommunications network 2613. Through a communications network 2613, theMSM controller is accessible through remote clients 2633 b (e.g.,computers with web browsers) by users 2633 a. Network interfaces mayemploy connection protocols such as, but not limited to: direct connect,Ethernet (thick, thin, twisted pair 10/100/1000/10000 Base T, and/or thelike), Token Ring, wireless connection such as IEEE 802.11a-x, and/orthe like. Should processing requirements dictate a greater amount speedand/or capacity, distributed network controllers (e.g., see DistributedMSM below), architectures may similarly be employed to pool, loadbalance, and/or otherwise decrease/increase the communicative bandwidthrequired by the MSM controller. A communications network may be any oneand/or the combination of the following: a direct interconnection; theInternet; Interplanetary Internet (e.g., Coherent File DistributionProtocol (CFDP), Space Communications Protocol Specifications (SCPS),etc.); a Local Area Network (LAN); a Metropolitan Area Network (MAN); anOperating Missions as Nodes on the Internet (OMNI); a secured customconnection; a Wide Area Network (WAN); a wireless network (e.g.,employing protocols such as, but not limited to a cellular, WiFi,Wireless Application Protocol (WAP), I-mode, and/or the like); and/orthe like. A network interface may be regarded as a specialized form ofan input output interface. Further, multiple network interfaces 2610 maybe used to engage with various communications network types 2613. Forexample, multiple network interfaces may be employed to allow for thecommunication over broadcast, multicast, and/or unicast networks.

Input Output interfaces (I/O) 2608 may accept, communicate, and/orconnect to user, peripheral devices 2612 (e.g., input devices 2611),cryptographic processor devices 2628, and/or the like. I/O may employconnection protocols such as, but not limited to: audio: analog,digital, monaural, RCA, stereo, and/or the like; data: Apple Desktop Bus(ADB), IEEE 1394a-b, serial, universal serial bus (USB); infrared;joystick; keyboard; midi; optical; PC AT; PS/2; parallel; radio; touchinterfaces: capacitive, optical, resistive, etc. displays; videointerface: Apple Desktop Connector (ADC), BNC, coaxial, component,composite, digital, Digital Visual Interface (DVI), (mini) displayport,high-definition multimedia interface (HDMI), RCA, RF antennae, S-Video,VGA, and/or the like; wireless transceivers: 802.11a/ac/b/g/n/x;Bluetooth; cellular (e.g., code division multiple access (CDMA), highspeed packet access (HSPA(+)), high-speed downlink packet access(HSDPA), global system for mobile communications (GSM), long termevolution (LTE), WiMax, etc.); and/or the like. One typical outputdevice may include a video display, which typically comprises a CathodeRay Tube (CRT) or Liquid Crystal Display (LCD) based monitor with aninterface (e.g., DVI circuitry and cable) that accepts signals from avideo interface, may be used. The video interface composites informationgenerated by a computer systemization and generates video signals basedon the composited information in a video memory frame. Another outputdevice is a television set, which accepts signals from a videointerface. Typically, the video interface provides the composited videoinformation through a video connection interface that accepts a videodisplay interface (e.g., an RCA composite video connector accepting anRCA composite video cable; a DVI connector accepting a DVI displaycable, etc.).

Peripheral devices 2612 may be connected and/or communicate to I/Oand/or other facilities of the like such as network interfaces, storageinterfaces, directly to the interface bus, system bus, the CPU, and/orthe like. Peripheral devices may be external, internal and/or part ofthe MSM controller. Peripheral devices may include: antenna, audiodevices (e.g., line-in, line-out, microphone input, speakers, etc.),cameras (e.g., gesture (e.g., Microsoft Kinect) detection, motiondetection, still, video, webcam, etc.), dongles (e.g., for copyprotection, ensuring secure transactions with a digital signature,and/or the like), external processors (for added capabilities; e.g.,crypto devices 528), force-feedback devices (e.g., vibrating motors),infrared (IR) transceiver, network interfaces, printers, scanners,sensors/sensor arrays and peripheral extensions (e.g., ambient light,GPS, gyroscopes, proximity, temperature, etc.), storage devices,transceivers (e.g., cellular, GPS, etc.), video devices (e.g., goggles,monitors, etc.), video sources, visors, and/or the like. Peripheraldevices often include types of input devices (e.g., cameras).

User input devices 2611 often are a type of peripheral device 512 (seeabove) and may include: card readers, dongles, finger print readers,gloves, graphics tablets, joysticks, keyboards, microphones, mouse(mice), remote controls, security/biometric devices (e.g., fingerprintreader, iris reader, retina reader, etc.), touch screens (e.g.,capacitive, resistive, etc.), trackballs, trackpads, styluses, and/orthe like.

It should be noted that although user input devices and peripheraldevices may be employed, the MSM controller may be embodied as anembedded, dedicated, and/or monitor-less (i.e., headless) device,wherein access would be provided over a network interface connection.

Cryptographic units such as, but not limited to, microcontrollers,processors 2626, interfaces 2627, and/or devices 2628 may be attached,and/or communicate with the MSM controller. A MC68HC16 microcontroller,manufactured by Motorola Inc., may be used for and/or withincryptographic units. The MC68HC16 microcontroller utilizes a 16-bitmultiply-and-accumulate instruction in the 16 MHz configuration andrequires less than one second to perform a 512-bit RSA private keyoperation. Cryptographic units support the authentication ofcommunications from interacting agents, as well as allowing foranonymous transactions. Cryptographic units may also be configured aspart of the CPU. Equivalent microcontrollers and/or processors may alsobe used. Other commercially available specialized cryptographicprocessors include: Broadcom's CryptoNetX and other Security Processors;nCipher's nShield; SafeNet's Luna PCI (e.g., 7100) series; SemaphoreCommunications' 40 MHz Roadrunner 184; Sun's Cryptographic Accelerators(e.g., Accelerator 6000 PCIe Board, Accelerator 500 Daughtercard); ViaNano Processor (e.g., L2100, L2200, U2400) line, which is capable ofperforming 500+MB/s of cryptographic instructions; VLSI Technology's 33MHz 6868; and/or the like.

Memory

Generally, any mechanization and/or embodiment allowing a processor toaffect the storage and/or retrieval of information is regarded as memory2629. However, memory is a fungible technology and resource, thus, anynumber of memory embodiments may be employed in lieu of or in concertwith one another. It is to be understood that the MSM controller and/ora computer systemization may employ various forms of memory 2629. Forexample, a computer systemization may be configured wherein theoperation of on-chip CPU memory (e.g., registers), RAM, ROM, and anyother storage devices are provided by a paper punch tape or paper punchcard mechanism; however, such an embodiment would result in an extremelyslow rate of operation. In a typical configuration, memory 2629 willinclude ROM 2606, RAM 2605, and a storage device 2614. A storage device2614 may be any conventional computer system storage. Storage devicesmay include: an array of devices (e.g., Redundant Array of IndependentDisks (RAID)); a drum; a (fixed and/or removable) magnetic disk drive; amagneto-optical drive; an optical drive (i.e., Blueray, CDROM/RAM/Recordable (R)/ReWritable (RW), DVD R/RW, HD DVD R/RW etc.); RAMdrives; solid state memory devices (USB memory, solid state drives(SSD), etc.); other processor-readable storage mediums; and/or otherdevices of the like. Thus, a computer systemization generally requiresand makes use of memory.

Component Collection

The memory 2629 may contain a collection of program and/or databasecomponents and/or data such as, but not limited to: operating systemcomponent(s) 2615 (operating system); information server component(s)2616 (information server); user interface component(s) 2617 (userinterface); Web browser component(s) 2618 (Web browser); database(s)2619; mail server component(s) 2621; mail client component(s) 2622;cryptographic server component(s) 2620 (cryptographic server); the MSMcomponent(s) 2635; and/or the like (i.e., collectively a componentcollection). These components may be stored and accessed from thestorage devices and/or from storage devices accessible through aninterface bus. Although non-conventional program components such asthose in the component collection, typically, are stored in a localstorage device 2614, they may also be loaded and/or stored in memorysuch as: peripheral devices, RAM, remote storage facilities through acommunications network, ROM, various forms of memory, and/or the like.

Operating System

The operating system component 2615 is an executable program componentfacilitating the operation of the MSM controller. Typically, theoperating system facilitates access of I/O, network interfaces,peripheral devices, storage devices, and/or the like. The operatingsystem may be a highly fault tolerant, scalable, and secure system suchas: Apple's Macintosh OS X (Server); AT&T Plan 9; Be OS; Blackberry'sQNX; Google's Chrome; Microsoft's Windows 7/8; Unix and Unix-like systemdistributions (such as AT&T's UNIX; Berkley Software Distribution (BSD)variations such as FreeBSD, NetBSD, OpenBSD, and/or the like; Linuxdistributions such as Red Hat, Ubuntu, and/or the like); and/or the likeoperating systems. However, more limited and/or less secure operatingsystems also may be employed such as Apple Macintosh OS, IBM OS/2,Microsoft DOS, Microsoft Windows2000/2003/3.1/95/98/CE/Millenium/Mobile/NT/Vista/XP (Server), Palm OS,and/or the like. Additionally, for robust mobile deploymentapplications, mobile operating systems may be used, such as: Apple'siOS; China Operating System COS; Google's Android; Microsoft WindowsRT/Phone; Palm's WebOS; Samsung/Intel's Tizen; and/or the like. Anoperating system may communicate to and/or with other components in acomponent collection, including itself, and/or the like. Mostfrequently, the operating system communicates with other programcomponents, user interfaces, and/or the like. For example, the operatingsystem may contain, communicate, generate, obtain, and/or provideprogram component, system, user, and/or data communications, requests,and/or responses. The operating system, once executed by the CPU, mayenable the interaction with communications networks, data, I/O,peripheral devices, program components, memory, user input devices,and/or the like. The operating system may provide communicationsprotocols that allow the MSM controller to communicate with otherentities through a communications network 2613. Various communicationprotocols may be used by the MSM controller as a subcarrier transportmechanism for interaction, such as, but not limited to: multicast,TCP/IP, UDP, unicast, and/or the like.

Information Server

An information server component 2616 is a stored program component thatis executed by a CPU. The information server may be a conventionalInternet information server such as, but not limited to Apache SoftwareFoundation's Apache, Microsoft's Internet Information Server, and/or thelike. The information server may allow for the execution of programcomponents through facilities such as Active Server Page (ASP), ActiveX,(ANSI) (Objective-) C (++), C# and/or .NET, Common Gateway Interface(CGI) scripts, dynamic (D) hypertext markup language (HTML), FLASH,Java, JavaScript, Practical Extraction Report Language (PERL), HypertextPre-Processor (PHP), pipes, Python, wireless application protocol (WAP),WebObjects, and/or the like. The information server may support securecommunications protocols such as, but not limited to, File TransferProtocol (FTP); HyperText Transfer Protocol (HTTP); Secure HypertextTransfer Protocol (HTTPS), Secure Socket Layer (SSL), messagingprotocols (e.g., America Online (AOL) Instant Messenger (AIM),Application Exchange (APEX), ICQ, Internet Relay Chat (IRC), MicrosoftNetwork (MSN) Messenger Service, Presence and Instant Messaging Protocol(PRIM), Internet Engineering Task Force's (IETF's) Session InitiationProtocol (SIP), SIP for Instant Messaging and Presence LeveragingExtensions (SIMPLE), open XML-based Extensible Messaging and PresenceProtocol (XMPP) (i.e., Jabber or Open Mobile Alliance's (OMA's) InstantMessaging and Presence Service (IMPS)), Yahoo! Instant MessengerService, and/or the like. The information server provides results in theform of Web pages to Web browsers, and allows for the manipulatedgeneration of the Web pages through interaction with other programcomponents. After a Domain Name System (DNS) resolution portion of anHTTP request is resolved to a particular information server, theinformation server resolves requests for information at specifiedlocations on the MSM controller based on the remainder of the HTTPrequest. For example, a request such ashttp://123.124.125.126/myInformation.html might have the IP portion ofthe request “123.124.125.126” resolved by a DNS server to an informationserver at that IP address; that information server might in turn furtherparse the http request for the “/myInformation.html” portion of therequest and resolve it to a location in memory containing theinformation “myInformation.html.” Additionally, other informationserving protocols may be employed across various ports, e.g., FTPcommunications across port 21, and/or the like. An information servermay communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Mostfrequently, the information server communicates with the MSM database2619, operating systems, other program components, user interfaces, Webbrowsers, and/or the like.

Access to the MSM database may be achieved through a number of databasebridge mechanisms such as through scripting languages as enumeratedbelow (e.g., CGI) and through inter-application communication channelsas enumerated below (e.g., CORBA, WebObjects, etc.). Any data requeststhrough a Web browser are parsed through the bridge mechanism intoappropriate grammars as required by the MSM. In one embodiment, theinformation server would provide a Web form accessible by a Web browser.Entries made into supplied fields in the Web form are tagged as havingbeen entered into the particular fields, and parsed as such. The enteredterms are then passed along with the field tags, which act to instructthe parser to generate queries directed to appropriate tables and/orfields. In one embodiment, the parser may generate queries in standardSQL by instantiating a search string with the proper join/selectcommands based on the tagged text entries, wherein the resulting commandis provided over the bridge mechanism to the MSM as a query. Upongenerating query results from the query, the results are passed over thebridge mechanism, and may be parsed for formatting and generation of anew results Web page by the bridge mechanism. Such a new results Webpage is then provided to the information server, which may supply it tothe requesting Web browser.

Also, an information server may contain, communicate, generate, obtain,and/or provide program component, system, user, and/or datacommunications, requests, and/or responses.

User Interface

Computer interfaces in some respects are similar to automobile operationinterfaces. Automobile operation interface elements such as steeringwheels, gearshifts, and speedometers facilitate the access, operation,and display of automobile resources, and status. Computer interactioninterface elements such as check boxes, cursors, menus, scrollers, andwindows (collectively and commonly referred to as widgets) similarlyfacilitate the access, capabilities, operation, and display of data andcomputer hardware and operating system resources, and status. Operationinterfaces are commonly called user interfaces. Graphical userinterfaces (GUIs) such as the Apple's iOS, Macintosh Operating System'sAqua; IBM's OS/2; Google's Chrome (e.g., and other webbrowser/cloudbased client OSs); Microsoft's Windows varied UIs2000/2003/3.1/95/98/CE/Millenium/Mobile/NT/Vista/XP (Server) (i.e.,Aero, Surface, etc.); Unix's X-Windows (e.g., which may includeadditional Unix graphic interface libraries and layers such as K DesktopEnvironment (KDE), mythTV and GNU Network Object Model Environment(GNOME)), web interface libraries (e.g., ActiveX, AJAX, (D)HTML, FLASH,Java, JavaScript, etc. interface libraries such as, but not limited to,Dojo, jQuery(UI), MooTools, Prototype, script.aculo.us, SWFObject,Yahoo! User Interface, any of which may be used and) provide a baselineand means of accessing and displaying information graphically to users.

A user interface component 2617 is a stored program component that isexecuted by a CPU. The user interface may be a conventional graphic userinterface as provided by, with, and/or atop operating systems and/oroperating environments such as already discussed. The user interface mayallow for the display, execution, interaction, manipulation, and/oroperation of program components and/or system facilities through textualand/or graphical facilities. The user interface provides a facilitythrough which users may affect, interact, and/or operate a computersystem. A user interface may communicate to and/or with other componentsin a component collection, including itself, and/or facilities of thelike. Most frequently, the user interface communicates with operatingsystems, other program components, and/or the like. The user interfacemay contain, communicate, generate, obtain, and/or provide programcomponent, system, user, and/or data communications, requests, and/orresponses.

Web Browser

A Web browser component 2618 is a stored program component that isexecuted by a CPU. The Web browser may be a conventional hypertextviewing application such as Apple's (mobile) Safari, Google's Chrome,Microsoft Internet Explorer, Mozilla's Firefox, Netscape Navigator,and/or the like. Secure Web browsing may be supplied with 128 bit (orgreater) encryption by way of HTTPS, SSL, and/or the like. Web browsersallowing for the execution of program components through facilities suchas ActiveX, AJAX, (D)HTML, FLASH, Java, JavaScript, web browser plug-inAPIs (e.g., FireFox, Safari Plug-in, and/or the like APIs), and/or thelike. Web browsers and like information access tools may be integratedinto PDAs, cellular telephones, and/or other mobile devices. A Webbrowser may communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Mostfrequently, the Web browser communicates with information servers,operating systems, integrated program components (e.g., plug-ins),and/or the like; e.g., it may contain, communicate, generate, obtain,and/or provide program component, system, user, and/or datacommunications, requests, and/or responses. Also, in place of a Webbrowser and information server, a combined application may be developedto perform similar operations of both. The combined application wouldsimilarly affect the obtaining and the provision of information tousers, user agents, and/or the like from the MSM enabled nodes. Thecombined application may be nugatory on systems employing standard Webbrowsers.

Mail Server

A mail server component 2621 is a stored program component that isexecuted by a CPU 2603. The mail server may be a conventional Internetmail server such as, but not limited to: dovecot, Courier IMAP, CyrusIMAP, Maildir, Microsoft Exchange, sendmail, and/or the like. The mailserver may allow for the execution of program components throughfacilities such as ASP, ActiveX, (ANSI) (Objective-) C (++), C# and/or.NET, CGI scripts, Java, JavaScript, PERL, PHP, pipes, Python,WebObjects, and/or the like. The mail server may support communicationsprotocols such as, but not limited to: Internet message access protocol(IMAP), Messaging Application Programming Interface (MAPI)/MicrosoftExchange, post office protocol (POP3), simple mail transfer protocol(SMTP), and/or the like. The mail server can route, forward, and processincoming and outgoing mail messages that have been sent, relayed and/orotherwise traversing through and/or to the MSM. Alternatively, the mailserver component may be distributed out to mail service providingentities such as Google's cloud services (e.g., Gmail and notificationsmay alternatively be provided via messenger services such as AOL'sInstant Messenger, Apple's iMessage, Google Messenger, SnapChat, etc.).

Access to the MSM mail may be achieved through a number of APIs offeredby the individual Web server components and/or the operating system.

Also, a mail server may contain, communicate, generate, obtain, and/orprovide program component, system, user, and/or data communications,requests, information, and/or responses.

Mail Client

A mail client component 2622 is a stored program component that isexecuted by a CPU 2603. The mail client may be a conventional mailviewing application such as Apple Mail, Microsoft Entourage, MicrosoftOutlook, Microsoft Outlook Express, Mozilla, Thunderbird, and/or thelike. Mail clients may support a number of transfer protocols, such as:IMAP, Microsoft Exchange, POP3, SMTP, and/or the like. A mail client maycommunicate to and/or with other components in a component collection,including itself, and/or facilities of the like. Most frequently, themail client communicates with mail servers, operating systems, othermail clients, and/or the like; e.g., it may contain, communicate,generate, obtain, and/or provide program component, system, user, and/ordata communications, requests, information, and/or responses. Generally,the mail client provides a facility to compose and transmit electronicmail messages.

Cryptographic Server

A cryptographic server component 2620 is a stored program component thatis executed by a CPU 2603, cryptographic processor 2626, cryptographicprocessor interface 2627, cryptographic processor device 2628, and/orthe like. Cryptographic processor interfaces will allow for expeditionof encryption and/or decryption requests by the cryptographic component;however, the cryptographic component, alternatively, may run on aconventional CPU. The cryptographic component allows for the encryptionand/or decryption of provided data. The cryptographic component allowsfor both symmetric and asymmetric (e.g., Pretty Good Protection (PGP))encryption and/or decryption. The cryptographic component may employcryptographic techniques such as, but not limited to: digitalcertificates (e.g., X.509 authentication framework), digital signatures,dual signatures, enveloping, password access protection, public keymanagement, and/or the like. The cryptographic component will facilitatenumerous (encryption and/or decryption) security protocols such as, butnot limited to: checksum, Data Encryption Standard (DES), EllipticalCurve Encryption (ECC), International Data Encryption Algorithm (IDEA),Message Digest 5 (MD5, which is a one way hash operation), passwords,Rivest Cipher (RC5), Rijndael, RSA (which is an Internet encryption andauthentication system that uses an algorithm developed in 1977 by RonRivest, Adi Shamir, and Leonard Adleman), Secure Hash Algorithm (SHA),Secure Socket Layer (SSL), Secure Hypertext Transfer Protocol (HTTPS),Transport Layer Security (TLS), and/or the like. Employing suchencryption security protocols, the MSM may encrypt all incoming and/oroutgoing communications and may serve as node within a virtual privatenetwork (VPN) with a wider communications network. The cryptographiccomponent facilitates the process of “security authorization” wherebyaccess to a resource is inhibited by a security protocol wherein thecryptographic component effects authorized access to the securedresource. In addition, the cryptographic component may provide uniqueidentifiers of content, e.g., employing and MD5 hash to obtain a uniquesignature for an digital audio file. A cryptographic component maycommunicate to and/or with other components in a component collection,including itself, and/or facilities of the like. The cryptographiccomponent supports encryption schemes allowing for the securetransmission of information across a communications network to enablethe MSM component to engage in secure transactions if so desired. Thecryptographic component facilitates the secure accessing of resources onthe MSM and facilitates the access of secured resources on remotesystems; i.e., it may act as a client and/or server of securedresources. Most frequently, the cryptographic component communicateswith information servers, operating systems, other program components,and/or the like. The cryptographic component may contain, communicate,generate, obtain, and/or provide program component, system, user, and/ordata communications, requests, and/or responses.

The MSM Database

The MSM database component 2619 may be embodied in a database and itsstored data. The database is a stored program component, which isexecuted by the CPU; the stored program component portion configuringthe CPU to process the stored data. The database may be a conventional,fault tolerant, relational, scalable, secure database such as MySQL,Oracle, Sybase, etc. may be used. Additionally, optimized fast memoryand distributed databases such as IBM's Netezza, MongoDB's MongoDB,opensource Hadoop, opensource VoltDB, SAP's Hana, etc. Relationaldatabases are an extension of a flat file. Relational databases consistof a series of related tables. The tables are interconnected via a keyfield. Use of the key field allows the combination of the tables byindexing against the key field; i.e., the key fields act as dimensionalpivot points for combining information from various tables.Relationships generally identify links maintained between tables bymatching primary keys. Primary keys represent fields that uniquelyidentify the rows of a table in a relational database. Alternative keyfields may be used from any of the fields having unique value sets, andin some alternatives, even non-unique values in combinations with otherfields. More precisely, they uniquely identify rows of a table on the“one” side of a one-to-many relationship.

Alternatively, the MSM database may be implemented using variousstandard data-structures, such as an array, hash, (linked) list, struct,structured text file (e.g., XML), table, and/or the like. Suchdata-structures may be stored in memory and/or in (structured) files. Inanother alternative, an object-oriented database may be used, such asFrontier, ObjectStore, Poet, Zope, and/or the like. Object databases caninclude a number of object collections that are grouped and/or linkedtogether by common attributes; they may be related to other objectcollections by some common attributes. Object-oriented databases performsimilarly to relational databases with the exception that objects arenot just pieces of data but may have other types of capabilitiesencapsulated within a given object. If the MSM database is implementedas a data-structure, the use of the MSM database 2619 may be integratedinto another component such as the MSM component 2635. Also, thedatabase may be implemented as a mix of data structures, objects, andrelational structures. Databases may be consolidated and/or distributedin countless variations (e.g., see Distributed MSM below). Portions ofdatabases, e.g., tables, may be exported and/or imported and thusdecentralized and/or integrated.

In one embodiment, the database component 2619 includes several tables2619 a-o:

An accounts table 2619 a includes fields such as, but not limited to: anaccountID, accountOwnerID, accountContactID, assetIDs, deviceIDs,paymentIDs, transactionIDs, userIDs, accountType (e.g., agent, entity(e.g., corporate, non-profit, partnership, etc.), individual, etc.),accountCreationDate, accountUpdateDate, accountName, accountNumber,routingNumber, linkWalletsID, accountPrioritAccaountRatio,accountAddress, accountState, accountZIPcode, accountCountry,accountEmail, accountPhone, accountAuthKey, accountIPaddres s,accountURLAccessCode, accountPortNo, accountAuthorizationCode,accountAccessPrivileges, accountPreferences, accountRestrictions, and/orthe like;

A users table 2619 b includes fields such as, but not limited to: auserID, userSSN, taxID, userContactID, accountID, assetIDs, deviceIDs,paymentIDs, transactionIDs, userType (e.g., agent, entity (e.g.,corporate, non-profit, partnership, etc.), individual, etc.),namePrefix, firstName, middleName, lastName, nameSuffix, DateOfBirth,userAge, userName, userEmail, userSocialAccountID, contactType,contactRelationship, userPhone, userAddress, userCity, userState,userZIPCode, userCountry, userAuthorizationCode, userAccessPrivilges,userPreferences, userRestrictions, and/or the like (the user table maysupport and/or track multiple entity accounts on a MSM);

An devices table 2619 c includes fields such as, but not limited to:deviceID, sensorIDs, accountID, assetIDs, paymentIDs, deviceType,deviceName, deviceManufacturer, deviceModel, deviceVersion,deviceSerialNo, deviceIPaddress, deviceMACaddress, device_ECID,deviceUUID, deviceLocation, deviceCertificate, deviceOS, appIDs,deviceResources, deviceSession, authKey, deviceSecureKey,walletAppInstalledFlag, deviceAccessPrivileges, devicePreferences,deviceRestrictions, hardware_config, software_config, storage_location,sensor_value, pin_reading, data_length, channel_requirement,sensor_name, sensor_model_no, sensor_manufacturer, sensor_type,sensor_serial_number, sensor_power_requirement,device_power_requirement, location, sensor_associated_tool,sensor_dimensions, device_dimensions, sensor_communications_type,device_communications_type, power_percentage, power_condition,temperature_setting, speed_adjust, hold_duration, part_actuation, and/orthe like. Device table may, in some embodiments, include fieldscorresponding to one or more Bluetooth profiles, such as those publishedat https://www.bluetooth.org/en-us/specification/adopted-specifications,and/or other device specifications, and/or the like;

An apps table 2619 d includes fields such as, but not limited to: appID,appName, appType, appDependencies, accountID, deviceIDs, transactionID,userID, appStoreAuthKey, appStoreAccountID, appStoreIPaddress,appStoreURLaccessCode, appStorePortNo, appAccessPrivileges,appPreferences, appRestrictions, portNum, access_API_call,linked_wallets_list, and/or the like;

An assets table 2619 e includes fields such as, but not limited to:assetID, accountID, userID, distributorAccountID, distributorPaymentID,distributorOnwerID, assetOwnerID, assetType, assetSourceDeviceID,assetSourceDeviceType, assetSourceDeviceName,assetSourceDistributionChannelID, assetSourceDistributionChannelType,assetSourceDistributionChannelName, assetTargetChannelID,assetTargetChannelType, assetTargetChannelName, assetName,assetSeriesName, assetSeriesSeason, assetSeriesEpisode, assetCode,assetQuantity, assetCost, assetPrice, assetValue, assetManufactuer,assetModelNo, assetSerialNo, assetLocation, assetAddress, assetState,assetZIPcode, assetState, assetCountry, assetEmail, assetIPaddress,assetURLaccessCode, assetOwnerAccountID, subscriptionIDs,assetAuthroizationCode, assetAccessPrivileges, assetPreferences,assetRestrictions, assetAPI, assetAPIconnectionAddress, and/or the like;

A payments table 2619 f includes fields such as, but not limited to:paymentID, accountID, userID, couponID, couponValue, couponConditions,couponExpiration, paymentType, paymentAccountNo, paymentAccountName,paymentAccountAuthorizationCodes, paymentExpirationDate, paymentCCV,paymentRoutingNo, paymentRoutingType, paymentAddress, paymentState,paymentZIPcode, paymentCountry, paymentEmail, paymentAuthKey,paymentIPaddress, paymentURLaccessCode, paymentPortNo,paymentAccessPrivileges, paymentPreferences, payementRestrictions,and/or the like;

An transactions table 2619 g includes fields such as, but not limitedto: transactionID, accountID, assetIDs, deviceIDs, paymentIDs,transactionIDs, userID, merchantID, transactionType, transactionDate,transactionTime, transactionAmount, transactionQuantity,transactionDetails, productsList, productType, productTitle,productsSummary, productParamsList, transactionNo,transactionAccessPrivileges, transactionPreferences,transactionRestrictions, merchantAuthKey, merchantAuthCode, and/or thelike;

An merchants table 2619 h includes fields such as, but not limited to:merchantID, merchantTaxID, merchanteName, merchantContactUserID,accountID, issuerID, acquirerID, merchantEmail, merchantAddress,merchantState, merchantZIPcode, merchantCountry, merchantAuthKey,merchantIPaddress, portNum, merchantURLaccessCode, merchantPortNo,merchantAccessPrivileges, merchantPreferences, merchantRestrictions,and/or the like;

An ads table 2619 i includes fields such as, but not limited to: adID,advertiserID, adMerchantID, adNetworkID, adName, adTags, advertiserName,adSponsor, adTime, adGeo, adAttributes, adFormat, adProduct, adText,adMedia, adMediaID, adChannelID, adTagTime, adAudioSignature, adHash,adTemplateID, adTemplateData, adSourceID, adSourceName,adSourceServerIP, adSourceURL, adSourceSecurityProtocol, adSourceFTP,adAuthKey, adAccessPrivileges, adPreferences, adRestrictions,adNetworkXchangeID, adNetworkXchangeName, adNetworkXchangeCost,adNetworkXchangeMetricType (e.g., CPA, CPC, CPM, CTR, etc.),adNetworkXchangeMetricValue, adNetworkXchangeServer,adNetworkXchangePortNumber, publisherID, publisherAddress, publisherURL,publisherTag, publisherindustry, publisherName, publisherDescription,siteDomain, siteURL, siteContent, siteTag, siteContext, sitelmpression,siteVisits, siteHeadline, sitePage, siteAdPrice, sitePlacement,sitePosition, bidID, bidExchange, bidOS, bidTarget, bidTimestamp,bidPrice, bidImpressionID, bidType, bidScore, adType (e.g., mobile,desktop, wearable, largescreen, interstitial, etc.), assetID,merchantID, deviceID, userID, accountID, impressionID, impressionOS,impressionTimeStamp, impressionGeo, impressionAction, impressionType,impressionPublisherID, impressionPublisherURL, and/or the like;

A message indexing table 2619 j includes fields such as, but not limitedto: messageID, messageTeam, messageChannel, messageUser, messageTopics,messageResponses, messageFileIDs, messageThirdPartyMetadata,messageConversationPrimitiveData, messageText, calculatedML_InputData,and/or the like;

A file indexing table 2619 k includes fields such as, but not limitedto: fileID, fileContents, and/or the like;

A MSM message table 26191 includes fields such as, but not limited to:messageID, messageContents, messageResponses, and/or the like;

A work graphs table 2619 m includes fields such as, but not limited to:workGraphID, workGraphTeamID, workGraphUserData, workGraphChanneIData,workGraphTopicData, and/or the like;

A ML structure inputs table 2619 n includes fields such as, but notlimited to: ML_StructureInputID, ML_StructureInputTeamID,ML_StructureInputName, ML_StructureInputType, ML_StructureInputData,and/or the like;

A ML structures table 26190 includes fields such as, but not limited to:ML_StructureID, ML_StructureTeamID, ML_StructureApplication,ML_StructureType, ML_StructureInputs, ML_StructureOutput,ML_StructureParametersData, and/or the like.

In one embodiment, the MSM database may interact with other databasesystems. For example, employing a distributed database system, queriesand data access by search MSM component may treat the combination of theMSM database, an integrated data security layer database as a singledatabase entity (e.g., see Distributed MSM below).

In one embodiment, user programs may contain various user interfaceprimitives, which may serve to update the MSM. Also, various accountsmay require custom database tables depending upon the environments andthe types of clients the MSM may need to serve. It should be noted thatany unique fields may be designated as a key field throughout. In analternative embodiment, these tables have been decentralized into theirown databases and their respective database controllers (i.e.,individual database controllers for each of the above tables). Employingstandard data processing techniques, one may further distribute thedatabases over several computer systemizations and/or storage devices.Similarly, configurations of the decentralized database controllers maybe varied by consolidating and/or distributing the various databasecomponents 2619 a-o. The MSM may be configured to keep track of varioussettings, inputs, and parameters via database controllers.

The MSM database may communicate to and/or with other components in acomponent collection, including itself, and/or facilities of the like.Most frequently, the MSM database communicates with the MSM component,other program components, and/or the like. The database may contain,retain, and provide information regarding other nodes and data.

The MSMs

The MSM component 2635 is a stored program component that is executed bya CPU. In one embodiment, the MSM component incorporates any and/or allcombinations of the aspects of the MSM that was discussed in theprevious figures. As such, the MSM affects accessing, obtaining and theprovision of information, services, transactions, and/or the like acrossvarious communications networks. The features and embodiments of the MSMdiscussed herein increase network efficiency by reducing data transferrequirements the use of more efficient data structures and mechanismsfor their transfer and storage. As a consequence, more data may betransferred in less time, and latencies with regard to transactions, arealso reduced. In many cases, such reduction in storage, transfer time,bandwidth requirements, latencies, etc., will reduce the capacity andstructural infrastructure requirements to support the MSM's features andfacilities, and in many cases reduce the costs, energyconsumption/requirements, and extend the life of MSM's underlyinginfrastructure; this has the added benefit of making the MSM morereliable. Similarly, many of the features and mechanisms are designed tobe easier for users to use and access, thereby broadening the audiencethat may enjoy/employ and exploit the feature sets of the MSM; such easeof use also helps to increase the reliability of the MSM. In addition,the feature sets include heightened security as noted via theCryptographic components 2620, 2626, 2628 and throughout, making accessto the features and data more reliable and secure.

The MSM transforms message, ranking request inputs, via MSM components(e.g., MD, WGG, MLSG, RD), into work graphs, ML structure input data, MLstructure, ranking response outputs.

The MSM component enabling access of information between nodes may bedeveloped by employing standard development tools and languages such as,but not limited to: Apache components, Assembly, ActiveX, binaryexecutables, (ANSI) (Objective-) C (++), C# and/or .NET, databaseadapters, CGI scripts, Java, JavaScript, mapping tools, procedural andobject oriented development tools, PERL, PHP, Python, shell scripts, SQLcommands, web application server extensions, web developmentenvironments and libraries (e.g., Microsoft's ActiveX; Adobe AIR, FLEX &FLASH; AJAX; (D)HTML; Dojo, Java; JavaScript; jQuery(UI); MooTools;Prototype; script.aculo.us; Simple Object Access Protocol (SOAP);SWFObject; Yahoo! User Interface; and/or the like), WebObjects, and/orthe like. In one embodiment, the MSM server employs a cryptographicserver to encrypt and decrypt communications. The MSM component maycommunicate to and/or with other components in a component collection,including itself, and/or facilities of the like. Most frequently, theMSM component communicates with the MSM database, operating systems,other program components, and/or the like. The MSM may contain,communicate, generate, obtain, and/or provide program component, system,user, and/or data communications, requests, and/or responses.

Distributed MSMs

The structure and/or operation of any of the MSM node controllercomponents may be combined, consolidated, and/or distributed in anynumber of ways to facilitate development and/or deployment. Similarly,the component collection may be combined in any number of ways tofacilitate deployment and/or development. To accomplish this, one mayintegrate the components into a common code base or in a facility thatcan dynamically load the components on demand in an integrated fashion.As such a combination of hardware may be distributed within a location,within a region and/or globally where logical access to a controller maybe abstracted as a singular node, yet where a multitude of private,semiprivate and publically accessible node controllers (e.g., viadispersed data centers) are coordinated to serve requests (e.g.,providing private cloud, semi-private cloud, and public cloud computingresources) and allowing for the serving of such requests in discreteregions (e.g., isolated, local, regional, national, global cloudaccess).

The component collection may be consolidated and/or distributed incountless variations through standard data processing and/or developmenttechniques. Multiple instances of any one of the program components inthe program component collection may be instantiated on a single node,and/or across numerous nodes to improve performance throughload-balancing and/or data-processing techniques. Furthermore, singleinstances may also be distributed across multiple controllers and/orstorage devices; e.g., databases. All program component instances andcontrollers working in concert may do so through standard dataprocessing communication techniques.

The configuration of the MSM controller will depend on the context ofsystem deployment. Factors such as, but not limited to, the budget,capacity, location, and/or use of the underlying hardware resources mayaffect deployment requirements and configuration. Regardless of if theconfiguration results in more consolidated and/or integrated programcomponents, results in a more distributed series of program components,and/or results in some combination between a consolidated anddistributed configuration, data may be communicated, obtained, and/orprovided. Instances of components consolidated into a common code basefrom the program component collection may communicate, obtain, and/orprovide data. This may be accomplished through intra-application dataprocessing communication techniques such as, but not limited to: datareferencing (e.g., pointers), internal messaging, object instancevariable communication, shared memory space, variable passing, and/orthe like. For example, cloud services such as Amazon Data Services,Microsoft Azure, Hewlett Packard Helion, IBM Cloud services allow forMSM controller and/or MSM component collections to be hosted in full orpartially for varying degrees of scale.

If component collection components are discrete, separate, and/orexternal to one another, then communicating, obtaining, and/or providingdata with and/or to other component components may be accomplishedthrough inter-application data processing communication techniques suchas, but not limited to: Application Program Interfaces (API) informationpassage; (distributed) Component Object Model ((D)COM), (Distributed)Object Linking and Embedding ((D)OLE), and/or the like), Common ObjectRequest Broker Architecture (CORBA), Jini local and remote applicationprogram interfaces, JavaScript Object Notation (JSON), Remote MethodInvocation (RMI), SOAP, process pipes, shared files, and/or the like.Messages sent between discrete component components forinter-application communication or within memory spaces of a singularcomponent for intra-application communication may be facilitated throughthe creation and parsing of a grammar. A grammar may be developed byusing development tools such as lex, yacc, XML, and/or the like, whichallow for grammar generation and parsing capabilities, which in turn mayform the basis of communication messages within and between components.

For example, a grammar may be arranged to recognize the tokens of anHTTP post command, e.g.:

-   -   w3c-post http:// . . . Value1

where Value1 is discerned as being a parameter because “http://” is partof the grammar syntax, and what follows is considered part of the postvalue. Similarly, with such a grammar, a variable “Value1” may beinserted into an “http://” post command and then sent. The grammarsyntax itself may be presented as structured data that is interpretedand/or otherwise used to generate the parsing mechanism (e.g., a syntaxdescription text file as processed by lex, yacc, etc.). Also, once theparsing mechanism is generated and/or instantiated, it itself mayprocess and/or parse structured data such as, but not limited to:character (e.g., tab) delineated text, HTML, structured text streams,XML, and/or the like structured data. In another embodiment,inter-application data processing protocols themselves may haveintegrated and/or readily available parsers (e.g., JSON, SOAP, and/orlike parsers) that may be employed to parse (e.g., communications) data.Further, the parsing grammar may be used beyond message parsing, but mayalso be used to parse: databases, data collections, data stores,structured data, and/or the like. Again, the desired configuration willdepend upon the context, environment, and requirements of systemdeployment.

For example, in some implementations, the MSM controller may beexecuting a PHP script implementing a Secure Sockets Layer (“SSL”)socket server via the information server, which listens to incomingcommunications on a server port to which a client may send data, e.g.,data encoded in JSON format. Upon identifying an incoming communication,the PHP script may read the incoming message from the client device,parse the received JSON-encoded text data to extract information fromthe JSON-encoded text data into PHP script variables, and store the data(e.g., client identifying information, etc.) and/or extractedinformation in a relational database accessible using the StructuredQuery Language (“SQL”). An exemplary listing, written substantially inthe form of PHP/SQL commands, to accept JSON-encoded input data from aclient device via a SSL connection, parse the data to extract variables,and store the data to a database, is provided below:

-   -   <?PHP    -   header(‘Content-Type: text/plain’);    -   // set ip address and port to listen to for incoming data    -   $address=‘192.168.0.100’;    -   $port=255;    -   // create a server-side SSL socket, listen for/accept incoming        communication    -   $sock=socket_create(AF_INET, SOCK_STREAM, 0);    -   socket_bind($sock, $address, $port) or die(‘Could not bind to        address’);    -   socket_listen($sock);    -   $client=socket_accept($sock);    -   // read input data from client device in 1024 byte blocks until        end of message do {        -   $input=“ ”;        -   $input=socket_read($client, 1024);        -   $data.=$input;    -   } while($input !=“ ”);    -   // parse data to extract variables    -   $obj=json_decode($data, true);    -   // store input data in a database    -   mysql_connect(“201.408.185.132”,$DBserver,$password); // access        database server    -   mysql_select(“CLIENT_DB.SQL”); // select database to append    -   mysql_query(“INSERT INTO UserTable (transmission)    -   VALUES ($data)”); // add data to UserTable table in a CLIENT        database    -   mysql_close(“CLIENT_DB.SQL”); // close connection to database    -   ?>

Also, the following resources may be used to provide example embodimentsregarding SOAP parser implementation:

-   -   http://www.xay.com/perl/site/lib/SOAP/Parser.html    -   http://publib.boulder.ibm.com/infocenter/tivihelp/v2r1/index.jsp?topic=/com.ibm.I        BMDI.doc/referenceguide295.htm        and other parser implementations:    -   http://publib.boulder.ibm.com/infocenter/tivihelp/v2r1/index.jsp?topic=/com.ibm.I        BMDI.doc/referenceguide259.htm        all of which are hereby expressly incorporated by reference.

Additional embodiments include:

-   1. A message indexing apparatus, comprising:-   a memory;-   a component collection in the memory, including:    -   a metadata determining component;-   a processor disposed in communication with the memory, and    configured to issue a plurality of processing instructions from the    component collection stored in the memory,    -   wherein the processor issues instructions from the metadata        determining component, stored in the memory, to:        -   obtain, via at least one processor, a metadata access            control carrying message;        -   determine, via at least one processor, message access            control data associated with the metadata access control            carrying message by analyzing metadata associated with the            metadata access control carrying message, wherein the            message access control data includes group level access            control data and channel level access control data;        -   determine, via at least one processor, a user identifier of            the user who sent the metadata access control carrying            message by analyzing metadata associated with the metadata            access control carrying message;        -   determine, via at least one processor, a set of topics            associated with the metadata access control carrying message            by analyzing message contents of the metadata access control            carrying message;        -   generate, via at least one processor, a group level message            index for the metadata access control carrying message,            wherein the group level message index's access control data            corresponds to the group level access control data, wherein            the metadata access control carrying message is indexed            using the determined message access control data, user            identifier, and set of topics such that the group level            message index facilitates searching using the indexed data.-   2. The apparatus of embodiment 1, further, comprising:    -   the processor issues instructions from the metadata determining        component, stored in the memory, to:        -   determine, via at least one processor, a set of responses            associated with the metadata access control carrying            message;        -   generate, via at least one processor, response index data            for the metadata access control carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the generated response index            data.-   3. The apparatus of embodiment 2, wherein a response is any of: a    reaction to the metadata access control carrying message, clicking    on a link in the metadata access control carrying message, replying    to the metadata access control carrying message, downloading a file    associated with the metadata access control carrying message,    sharing the metadata access control carrying message to another    channel, pinning the metadata access control carrying message,    starring the metadata access control carrying message.-   4. The apparatus of embodiment 2, wherein the response index data    includes a set of user identifiers of users who responded to the    metadata access control carrying message.-   5. The apparatus of embodiment 2, wherein the response index data    includes a social score generated for the metadata access control    carrying message based on the set of responses.-   6. The apparatus of embodiment 1, further, comprising:    -   the processor issues instructions from the metadata determining        component, stored in the memory, to:        -   determine, via at least one processor, a set of files            associated with the metadata access control carrying message            by analyzing metadata associated with the metadata access            control carrying message;        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the set of files; and        -   generate, via at least one processor, a group level file            index for the set of files, wherein the group level file            index's access control data corresponds to the group level            access control data.-   7. The apparatus of embodiment 1, further, comprising:    -   the processor issues instructions from the metadata determining        component, stored in the memory, to:        -   determine, via at least one processor, third party metadata            associated with the metadata access control carrying message            by analyzing metadata associated with the metadata access            control carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the third party metadata.-   8. The apparatus of embodiment 1, further, comprising:    -   the processor issues instructions from the metadata determining        component, stored in the memory, to:        -   determine, via at least one processor, a conversation            primitive associated with the metadata access control            carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is indexed using the conversation primitive.-   9. The apparatus of embodiment 8, wherein the conversation primitive    is the metadata access control carrying message, a specified number    of preceding metadata access control carrying messages and a    specified number of following metadata access control carrying    messages.-   10. The apparatus of embodiment 8, wherein the conversation    primitive is the metadata access control carrying message and a set    of other metadata access control carrying messages determined based    on message send time proximity to the metadata access control    carrying message.-   11. The apparatus of embodiment 1, wherein the group level access    control data is associated with an organization group.-   12. The apparatus of embodiment 1, wherein the group level access    control data is associated with a team group.-   13. The apparatus of embodiment 1, further, comprising:    -   a ranking determining component in the component collection, and    -   the processor issues instructions from the ranking determining        component, stored in the memory, to:        -   obtain, via at least one processor, a ranking request            associated with a ranking application; and        -   determine, via at least one processor, that the metadata            access control carrying message is relevant to the ranking            request based on analysis of the associated indexed            metadata.-   14. The apparatus of embodiment 13, wherein the ranking request is a    search query from a user who is allowed access to the metadata    access control carrying message.-   15. The apparatus of embodiment 13, wherein the ranking application    is any of: ranking metadata access control carrying messages,    ranking people, ranking channels.-   16. A processor-readable message indexing non-transient physical    medium storing processor-executable components, the components,    comprising:-   a component collection stored in the medium, including:    -   a metadata determining component;    -   wherein the metadata determining component, stored in the        medium, includes processor-issuable instructions to:        -   obtain, via at least one processor, a metadata access            control carrying message;        -   determine, via at least one processor, message access            control data associated with the metadata access control            carrying message by analyzing metadata associated with the            metadata access control carrying message, wherein the            message access control data includes group level access            control data and channel level access control data;        -   determine, via at least one processor, a user identifier of            the user who sent the metadata access control carrying            message by analyzing metadata associated with the metadata            access control carrying message;        -   determine, via at least one processor, a set of topics            associated with the metadata access control carrying message            by analyzing message contents of the metadata access control            carrying message;        -   generate, via at least one processor, a group level message            index for the metadata access control carrying message,            wherein the group level message index's access control data            corresponds to the group level access control data, wherein            the metadata access control carrying message is indexed            using the determined message access control data, user            identifier, and set of topics such that the group level            message index facilitates searching using the indexed data.-   17. The medium of embodiment 16, further, comprising:    -   the metadata determining component, stored in the medium,        includes processor-issuable instructions to:        -   determine, via at least one processor, a set of responses            associated with the metadata access control carrying            message;        -   generate, via at least one processor, response index data            for the metadata access control carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the generated response index            data.-   18. The medium of embodiment 17, wherein a response is any of: a    reaction to the metadata access control carrying message, clicking    on a link in the metadata access control carrying message, replying    to the metadata access control carrying message, downloading a file    associated with the metadata access control carrying message,    sharing the metadata access control carrying message to another    channel, pinning the metadata access control carrying message,    starring the metadata access control carrying message.-   19. The medium of embodiment 17, wherein the response index data    includes a set of user identifiers of users who responded to the    metadata access control carrying message.-   20. The medium of embodiment 17, wherein the response index data    includes a social score generated for the metadata access control    carrying message based on the set of responses.-   21. The medium of embodiment 16, further, comprising:    -   the metadata determining component, stored in the medium,        includes processor-issuable instructions to:        -   determine, via at least one processor, a set of files            associated with the metadata access control carrying message            by analyzing metadata associated with the metadata access            control carrying message;        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the set of files; and        -   generate, via at least one processor, a group level file            index for the set of files, wherein the group level file            index's access control data corresponds to the group level            access control data.-   22. The medium of embodiment 16, further, comprising:    -   the metadata determining component, stored in the medium,        includes processor-issuable instructions to:        -   determine, via at least one processor, third party metadata            associated with the metadata access control carrying message            by analyzing metadata associated with the metadata access            control carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the third party metadata.-   23. The medium of embodiment 16, further, comprising:    -   the metadata determining component, stored in the medium,        includes processor-issuable instructions to:        -   determine, via at least one processor, a conversation            primitive associated with the metadata access control            carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is indexed using the conversation primitive.-   24. The medium of embodiment 23, wherein the conversation primitive    is the metadata access control carrying message, a specified number    of preceding metadata access control carrying messages and a    specified number of following metadata access control carrying    messages.-   25. The medium of embodiment 23, wherein the conversation primitive    is the metadata access control carrying message and a set of other    metadata access control carrying messages determined based on    message send time proximity to the metadata access control carrying    message.-   26. The medium of embodiment 16, wherein the group level access    control data is associated with an organization group.-   27. The medium of embodiment 16, wherein the group level access    control data is associated with a team group.-   28. The medium of embodiment 16, further, comprising:    -   a ranking determining component in the component collection;    -   wherein the ranking determining component, stored in the medium,        includes processor-issuable instructions to:        -   obtain, via at least one processor, a ranking request            associated with a ranking application; and        -   determine, via at least one processor, that the metadata            access control carrying message is relevant to the ranking            request based on analysis of the associated indexed            metadata.-   29. The medium of embodiment 28, wherein the ranking request is a    search query from a user who is allowed access to the metadata    access control carrying message.-   30. The medium of embodiment 28, wherein the ranking application is    any of: ranking metadata access control carrying messages, ranking    people, ranking channels.-   31. A processor-implemented message indexing system, comprising:    -   a metadata determining component means, to:        -   obtain, via at least one processor, a metadata access            control carrying message;        -   determine, via at least one processor, message access            control data associated with the metadata access control            carrying message by analyzing metadata associated with the            metadata access control carrying message, wherein the            message access control data includes group level access            control data and channel level access control data;        -   determine, via at least one processor, a user identifier of            the user who sent the metadata access control carrying            message by analyzing metadata associated with the metadata            access control carrying message;        -   determine, via at least one processor, a set of topics            associated with the metadata access control carrying message            by analyzing message contents of the metadata access control            carrying message;        -   generate, via at least one processor, a group level message            index for the metadata access control carrying message,            wherein the group level message index's access control data            corresponds to the group level access control data, wherein            the metadata access control carrying message is indexed            using the determined message access control data, user            identifier, and set of topics such that the group level            message index facilitates searching using the indexed data.-   32. The system of embodiment 31, further, comprising:    -   the metadata determining component means, to:        -   determine, via at least one processor, a set of responses            associated with the metadata access control carrying            message;        -   generate, via at least one processor, response index data            for the metadata access control carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the generated response index            data.-   33. The system of embodiment 32, wherein a response is any of: a    reaction to the metadata access control carrying message, clicking    on a link in the metadata access control carrying message, replying    to the metadata access control carrying message, downloading a file    associated with the metadata access control carrying message,    sharing the metadata access control carrying message to another    channel, pinning the metadata access control carrying message,    starring the metadata access control carrying message.-   34. The system of embodiment 32, wherein the response index data    includes a set of user identifiers of users who responded to the    metadata access control carrying message.-   35. The system of embodiment 32, wherein the response index data    includes a social score generated for the metadata access control    carrying message based on the set of responses.-   36. The system of embodiment 31, further, comprising:    -   the metadata determining component means, to:        -   determine, via at least one processor, a set of files            associated with the metadata access control carrying message            by analyzing metadata associated with the metadata access            control carrying message;        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the set of files; and        -   generate, via at least one processor, a group level file            index for the set of files, wherein the group level file            index's access control data corresponds to the group level            access control data.-   37. The system of embodiment 31, further, comprising:    -   the metadata determining component means, to:        -   determine, via at least one processor, third party metadata            associated with the metadata access control carrying message            by analyzing metadata associated with the metadata access            control carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the third party metadata.-   38. The system of embodiment 31, further, comprising:    -   the metadata determining component means, to:        -   determine, via at least one processor, a conversation            primitive associated with the metadata access control            carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is indexed using the conversation primitive.-   39. The system of embodiment 38, wherein the conversation primitive    is the metadata access control carrying message, a specified number    of preceding metadata access control carrying messages and a    specified number of following metadata access control carrying    messages.-   40. The system of embodiment 38, wherein the conversation primitive    is the metadata access control carrying message and a set of other    metadata access control carrying messages determined based on    message send time proximity to the metadata access control carrying    message.-   41. The system of embodiment 31, wherein the group level access    control data is associated with an organization group.-   42. The system of embodiment 31, wherein the group level access    control data is associated with a team group.-   43. The system of embodiment 31, further, comprising:    -   a ranking determining component means, to:        -   obtain, via at least one processor, a ranking request            associated with a ranking application; and        -   determine, via at least one processor, that the metadata            access control carrying message is relevant to the ranking            request based on analysis of the associated indexed            metadata.-   44. The system of embodiment 43, wherein the ranking request is a    search query from a user who is allowed access to the metadata    access control carrying message.-   45. The system of embodiment 43, wherein the ranking application is    any of: ranking metadata access control carrying messages, ranking    people, ranking channels.-   46. A processor-implemented message indexing method, comprising:    -   executing processor-implemented metadata determining component        instructions to:        -   obtain, via at least one processor, a metadata access            control carrying message;        -   determine, via at least one processor, message access            control data associated with the metadata access control            carrying message by analyzing metadata associated with the            metadata access control carrying message, wherein the            message access control data includes group level access            control data and channel level access control data;        -   determine, via at least one processor, a user identifier of            the user who sent the metadata access control carrying            message by analyzing metadata associated with the metadata            access control carrying message;        -   determine, via at least one processor, a set of topics            associated with the metadata access control carrying message            by analyzing message contents of the metadata access control            carrying message;        -   generate, via at least one processor, a group level message            index for the metadata access control carrying message,            wherein the group level message index's access control data            corresponds to the group level access control data, wherein            the metadata access control carrying message is indexed            using the determined message access control data, user            identifier, and set of topics such that the group level            message index facilitates searching using the indexed data.-   47. The method of embodiment 46, further, comprising:    -   executing processor-implemented metadata determining component        instructions to:        -   determine, via at least one processor, a set of responses            associated with the metadata access control carrying            message;        -   generate, via at least one processor, response index data            for the metadata access control carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the generated response index            data.-   48. The method of embodiment 47, wherein a response is any of: a    reaction to the metadata access control carrying message, clicking    on a link in the metadata access control carrying message, replying    to the metadata access control carrying message, downloading a file    associated with the metadata access control carrying message,    sharing the metadata access control carrying message to another    channel, pinning the metadata access control carrying message,    starring the metadata access control carrying message.-   49. The method of embodiment 47, wherein the response index data    includes a set of user identifiers of users who responded to the    metadata access control carrying message.-   50. The method of embodiment 47, wherein the response index data    includes a social score generated for the metadata access control    carrying message based on the set of responses.-   51. The method of embodiment 46, further, comprising:    -   executing processor-implemented metadata determining component        instructions to:        -   determine, via at least one processor, a set of files            associated with the metadata access control carrying message            by analyzing metadata associated with the metadata access            control carrying message;        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the set of files; and        -   generate, via at least one processor, a group level file            index for the set of files, wherein the group level file            index's access control data corresponds to the group level            access control data.-   52. The method of embodiment 46, further, comprising:    -   executing processor-implemented metadata determining component        instructions to:        -   determine, via at least one processor, third party metadata            associated with the metadata access control carrying message            by analyzing metadata associated with the metadata access            control carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is also indexed using the third party metadata.-   53. The method of embodiment 46, further, comprising:    -   executing processor-implemented metadata determining component        instructions to:        -   determine, via at least one processor, a conversation            primitive associated with the metadata access control            carrying message; and        -   generate, via at least one processor, the group level            message index for the metadata access control carrying            message such that the metadata access control carrying            message is indexed using the conversation primitive.-   54. The method of embodiment 53, wherein the conversation primitive    is the metadata access control carrying message, a specified number    of preceding metadata access control carrying messages and a    specified number of following metadata access control carrying    messages.-   55. The method of embodiment 53, wherein the conversation primitive    is the metadata access control carrying message and a set of other    metadata access control carrying messages determined based on    message send time proximity to the metadata access control-   56. The method of embodiment 46, wherein the group level access    control data is associated with an organization group.-   57. The method of embodiment 46, wherein the group level access    control data is associated with a team group.-   58. The method of embodiment 46, further, comprising:    -   executing processor-implemented ranking determining component        instructions to:        -   obtain, via at least one processor, a ranking request            associated with a ranking application; and        -   determine, via at least one processor, that the metadata            access control carrying message is relevant to the ranking            request based on analysis of the associated indexed            metadata.-   59. The method of embodiment 58, wherein the ranking request is a    search query from a user who is allowed access to the metadata    access control carrying message.-   60. The method of embodiment 58, wherein the ranking application is    any of: ranking metadata access control carrying messages, ranking    people, ranking channels.-   101. A work graph generating apparatus, comprising:-   a memory;-   a component collection in the memory, including:    -   a work graph generating component;-   a processor disposed in communication with the memory, and    configured to issue a plurality of processing instructions from the    component collection stored in the memory,    -   wherein the processor issues instructions from the work graph        generating component, stored in the memory, to:        -   obtain, via at least one processor, a work graph generation            request, wherein the work graph generation request includes            group level access control data;        -   determine, via at least one processor, a set of metadata            access control carrying messages, wherein access control            data associated with metadata access control carrying            messages in the set of metadata access control carrying            messages corresponds to the group level access control data;        -   determine, via at least one processor, a set of users,            wherein access control data associated with users in the set            of users corresponds to the group level access control data;    -   calculate, via at least one processor, from the perspective of        each user in the set of users, a user priority score for each of        the other users in the set of users, wherein a user priority        score from the perspective of a first user for a second user is        calculated based on the number of metadata access control        carrying messages, in the set of metadata access control        carrying messages, associated with the second user that were        user-pertinent to the first user;    -   determine, via at least one processor, a set of channels,        wherein access control data associated with channels in the set        of channels corresponds to the group level access control data;    -   calculate, via at least one processor, from the perspective of        each user in the set of users, a channel priority score for each        of the channels in the set of channels, wherein a channel        priority score from the perspective of a user for a channel is        calculated based on the number of metadata access control        carrying messages, in the set of metadata access control        carrying messages, associated with that channel that were        channel-pertinent to that user;    -   determine, via at least one processor, a set of topics        associated with the set of metadata access control carrying        messages;    -   calculate, via at least one processor, from the perspective of        each user in the set of users, a topic priority score for each        of the topics in the set of topics, wherein a topic priority        score from the perspective of a user for a topic is calculated        based on the number of metadata access control carrying        messages, in the set of metadata access control carrying        messages, associated with that topic that were topic-pertinent        to that user; and    -   generate, via at least one processor, a work graph data        structure that includes a set of user objects corresponding to        the set of users, wherein a user object for a user includes data        regarding the calculated user priority scores, channel priority        scores, and topic priority scores associated with that user, and        wherein the work graph data structure is associated with group        level access control data.-   102. The apparatus of embodiment 101, wherein the group level access    control data is associated with an organization group.-   103. The apparatus of embodiment 101, wherein the group level access    control data is associated with a team group.-   104. The apparatus of embodiment 101, wherein the set of metadata    access control carrying messages is filtered to exclude metadata    access control carrying messages sent outside a specified time    period.-   105. The apparatus of embodiment 101, wherein the user-pertinent    metadata access control carrying messages are any of: metadata    access control carrying messages from the second user read by the    first user, metadata access control carrying messages from the first    user to the second user, metadata access control carrying messages    from the second user responded to by the first user.-   106. The apparatus of embodiment 105, wherein a response to a    metadata access control carrying message is any of: a reaction to    the metadata access control carrying message, clicking on a link in    the metadata access control carrying message, replying to the    metadata access control carrying message, downloading a file    associated with the metadata access control carrying message,    sharing the metadata access control carrying message to another    channel, pinning the metadata access control carrying message,    starring the metadata access control carrying message.-   107. The apparatus of embodiment 101, wherein the channel-pertinent    metadata access control carrying messages associated with a channel    for a user are any of: metadata access control carrying messages    sent by that user in that channel, metadata access control carrying    messages read by that user in that channel, metadata access control    carrying messages in that channel responded to by that user.-   108. The apparatus of embodiment 101, wherein the topic-pertinent    metadata access control carrying messages associated with a topic    for a user are any of: metadata access control carrying messages    sent by that user regarding that topic, metadata access control    carrying messages read by that user regarding that topic, metadata    access control carrying messages regarding that topic responded to    by that user.-   109. The apparatus of embodiment 101, further, comprising:    -   the processor issues instructions from the work graph generating        component, stored in the memory, to:        -   calculate, via at least one processor, from the perspective            of each channel in the set of channels, a channel similarity            score for each of the other channels in the set of channels,            wherein a channel similarity score from the perspective of a            first channel for a second channel is calculated based on            the number of common users, in the set of users, that the            first channel and the second channel share;        -   wherein the generated work graph structure includes a set of            channel objects corresponding to the set of channels,            wherein a channel object for a channel includes data            regarding the calculated channel similarity scores            associated with that channel.-   110. The apparatus of embodiment 101, further, comprising:    -   the processor issues instructions from the work graph generating        component, stored in the memory, to:        -   calculate, via at least one processor, from the perspective            of each topic in the set of topics, a topic similarity score            for each of the other topics in the set of topics;        -   wherein the generated work graph structure includes a set of            topic objects corresponding to the set of topics, wherein a            topic object for a topic includes data regarding the            calculated topic similarity scores associated with that            topic.-   111. The apparatus of embodiment 101, wherein user priority scores    between two users in the set of users are asymmetric.-   112. The apparatus of embodiment 101, wherein user priority scores    between two users in the set of users are symmetric.-   113. The apparatus of embodiment 101, further, comprising:    -   a machine learning structure generating component in the        component collection, and    -   the processor issues instructions from the machine learning        structure generating component, stored in the memory, to:        -   determine, via at least one processor, a ranking application            for which to generate a machine learning structure;        -   determine, via at least one processor, a set of inputs for            the machine learning structure based on the ranking            application, wherein at least some of the inputs in the set            of inputs correspond to work graph data stored in the work            graph data structure;        -   train, via at least one processor, the machine learning            structure using at least some of the work graph data stored            in the work graph data structure; and        -   store, via at least one processor, machine learning            structure parameters of the trained machine learning            structure, wherein the trained machine learning structure is            associated with group level access control data.-   114. The apparatus of embodiment 113, wherein the machine learning    structure is a neural network.-   115. The apparatus of embodiment 113, wherein the ranking    application is any of: ranking metadata access control carrying    messages, ranking people, ranking channels.-   116. A processor-readable work graph generating non-transient    physical medium storing processor-executable components, the    components, comprising:-   a component collection stored in the medium, including:    -   a work graph generating component;    -   wherein the work graph generating component, stored in the        medium, includes processor-issuable instructions to:        -   obtain, via at least one processor, a work graph generation            request, wherein the work graph generation request includes            group level access control data;        -   determine, via at least one processor, a set of metadata            access control carrying messages, wherein access control            data associated with metadata access control carrying            messages in the set of metadata access control carrying            messages corresponds to the group level access control data;        -   determine, via at least one processor, a set of users,            wherein access control data associated with users in the set            of users corresponds to the group level access control data;        -   calculate, via at least one processor, from the perspective            of each user in the set of users, a user priority score for            each of the other users in the set of users, wherein a user            priority score from the perspective of a first user for a            second user is calculated based on the number of metadata            access control carrying messages, in the set of metadata            access control carrying messages, associated with the second            user that were user-pertinent to the first user;        -   determine, via at least one processor, a set of channels,            wherein access control data associated with channels in the            set of channels corresponds to the group level access            control data;        -   calculate, via at least one processor, from the perspective            of each user in the set of users, a channel priority score            for each of the channels in the set of channels, wherein a            channel priority score from the perspective of a user for a            channel is calculated based on the number of metadata access            control carrying messages, in the set of metadata access            control carrying messages, associated with that channel that            were channel-pertinent to that user;        -   determine, via at least one processor, a set of topics            associated with the set of metadata access control carrying            messages;        -   calculate, via at least one processor, from the perspective            of each user in the set of users, a topic priority score for            each of the topics in the set of topics, wherein a topic            priority score from the perspective of a user for a topic is            calculated based on the number of metadata access control            carrying messages, in the set of metadata access control            carrying messages, associated with that topic that were            topic-pertinent to that user; and        -   generate, via at least one processor, a work graph data            structure that includes a set of user objects corresponding            to the set of users, wherein a user object for a user            includes data regarding the calculated user priority scores,            channel priority scores, and topic priority scores            associated with that user, and wherein the work graph data            structure is associated with group level access control            data.-   117. The medium of embodiment 116, wherein the group level access    control data is associated with an organization group.-   118. The medium of embodiment 116, wherein the group level access    control data is associated with a team group.-   119. The medium of embodiment 116, wherein the set of metadata    access control carrying messages is filtered to exclude metadata    access control carrying messages sent outside a specified time    period.-   120. The medium of embodiment 116, wherein the user-pertinent    metadata access control carrying messages are any of: metadata    access control carrying messages from the second user read by the    first user, metadata access control carrying messages from the first    user to the second user, metadata access control carrying messages    from the second user responded to by the first user.-   121. The medium of embodiment 120, wherein a response to a metadata    access control carrying message is any of: a reaction to the    metadata access control carrying message, clicking on a link in the    metadata access control carrying message, replying to the metadata    access control carrying message, downloading a file associated with    the metadata access control carrying message, sharing the metadata    access control carrying message to another channel, pinning the    metadata access control carrying message, starring the metadata    access control carrying message.-   122. The medium of embodiment 116, wherein the channel-pertinent    metadata access control carrying messages associated with a channel    for a user are any of: metadata access control carrying messages    sent by that user in that channel, metadata access control carrying    messages read by that user in that channel, metadata access control    carrying messages in that channel responded to by that user.-   123. The medium of embodiment 116, wherein the topic-pertinent    metadata access control carrying messages associated with a topic    for a user are any of: metadata access control carrying messages    sent by that user regarding that topic, metadata access control    carrying messages read by that user regarding that topic, metadata    access control carrying messages regarding that topic responded to    by that user.-   124. The medium of embodiment 116, further, comprising:    -   the work graph generating component, stored in the medium,        includes processor-issuable instructions to:        -   calculate, via at least one processor, from the perspective            of each channel in the set of channels, a channel similarity            score for each of the other channels in the set of channels,            wherein a channel similarity score from the perspective of a            first channel for a second channel is calculated based on            the number of common users, in the set of users, that the            first channel and the second channel share;        -   wherein the generated work graph structure includes a set of            channel objects corresponding to the set of channels,            wherein a channel object for a channel includes data            regarding the calculated channel similarity scores            associated with that channel.-   125. The medium of embodiment 116, further, comprising:    -   the work graph generating component, stored in the medium,        includes processor-issuable instructions to:        -   calculate, via at least one processor, from the perspective            of each topic in the set of topics, a topic similarity score            for each of the other topics in the set of topics;        -   wherein the generated work graph structure includes a set of            topic objects corresponding to the set of topics, wherein a            topic object for a topic includes data regarding the            calculated topic similarity scores associated with that            topic.-   126. The medium of embodiment 116, wherein user priority scores    between two users in the set of users are asymmetric.-   127. The medium of embodiment 116, wherein user priority scores    between two users in the set of users are symmetric.-   128. The medium of embodiment 116, further, comprising:    -   a machine learning structure generating component in the        component collection;    -   wherein the machine learning structure generating component,        stored in the medium, includes processor-issuable instructions        to:        -   determine, via at least one processor, a ranking application            for which to generate a machine learning structure;        -   determine, via at least one processor, a set of inputs for            the machine learning structure based on the ranking            application, wherein at least some of the inputs in the set            of inputs correspond to work graph data stored in the work            graph data structure;        -   train, via at least one processor, the machine learning            structure using at least some of the work graph data stored            in the work graph data structure; and        -   store, via at least one processor, machine learning            structure parameters of the trained machine learning            structure, wherein the trained machine learning structure is            associated with group level access control data.-   129. The medium of embodiment 128, wherein the machine learning    structure is a neural network.-   130. The medium of embodiment 128, wherein the ranking application    is any of: ranking metadata access control carrying messages,    ranking people, ranking channels.-   131. A processor-implemented work graph generating system,    comprising:    -   a work graph generating component means, to:        -   obtain, via at least one processor, a work graph generation            request, wherein the work graph generation request includes            group level access control data;        -   determine, via at least one processor, a set of metadata            access control carrying messages, wherein access control            data associated with metadata access control carrying            messages in the set of metadata access control carrying            messages corresponds to the group level access control data;        -   determine, via at least one processor, a set of users,            wherein access control data associated with users in the set            of users corresponds to the group level access control data;        -   calculate, via at least one processor, from the perspective            of each user in the set of users, a user priority score for            each of the other users in the set of users, wherein a user            priority score from the perspective of a first user for a            second user is calculated based on the number of metadata            access control carrying messages, in the set of metadata            access control carrying messages, associated with the second            user that were user-pertinent to the first user;        -   determine, via at least one processor, a set of channels,            wherein access control data associated with channels in the            set of channels corresponds to the group level access            control data;        -   calculate, via at least one processor, from the perspective            of each user in the set of users, a channel priority score            for each of the channels in the set of channels, wherein a            channel priority score from the perspective of a user for a            channel is calculated based on the number of metadata access            control carrying messages, in the set of metadata access            control carrying messages, associated with that channel that            were channel-pertinent to that user;        -   determine, via at least one processor, a set of topics            associated with the set of metadata access control carrying            messages;        -   calculate, via at least one processor, from the perspective            of each user in the set of users, a topic priority score for            each of the topics in the set of topics, wherein a topic            priority score from the perspective of a user for a topic is            calculated based on the number of metadata access control            carrying messages, in the set of metadata access control            carrying messages, associated with that topic that were            topic-pertinent to that user; and        -   generate, via at least one processor, a work graph data            structure that includes a set of user objects corresponding            to the set of users, wherein a user object for a user            includes data regarding the calculated user priority scores,            channel priority scores, and topic priority scores            associated with that user, and wherein the work graph data            structure is associated with group level access control            data.-   132. The system of embodiment 131, wherein the group level access    control data is associated with an organization group.-   133. The system of embodiment 131, wherein the group level access    control data is associated with a team group.-   134. The system of embodiment 131, wherein the set of metadata    access control carrying messages is filtered to exclude metadata    access control carrying messages sent outside a specified time    period.-   135. The system of embodiment 131, wherein the user-pertinent    metadata access control carrying messages are any of: metadata    access control carrying messages from the second user read by the    first user, metadata access control carrying messages from the first    user to the second user, metadata access control carrying messages    from the second user responded to by the first user.-   136. The system of embodiment 135, wherein a response to a metadata    access control carrying message is any of: a reaction to the    metadata access control carrying message, clicking on a link in the    metadata access control carrying message, replying to the metadata    access control carrying message, downloading a file associated with    the metadata access control carrying message, sharing the metadata    access control carrying message to another channel, pinning the    metadata access control carrying message, starring the metadata    access control carrying message.-   137. The system of embodiment 131, wherein the channel-pertinent    metadata access control carrying messages associated with a channel    for a user are any of: metadata access control carrying messages    sent by that user in that channel, metadata access control carrying    messages read by that user in that channel, metadata access control    carrying messages in that channel responded to by that user.-   138. The system of embodiment 131, wherein the topic-pertinent    metadata access control carrying messages associated with a topic    for a user are any of: metadata access control carrying messages    sent by that user regarding that topic, metadata access control    carrying messages read by that user regarding that topic, metadata    access control carrying messages regarding that topic responded to    by that user.-   139. The system of embodiment 131, further, comprising:    -   the work graph generating component means, to:        -   calculate, via at least one processor, from the perspective            of each channel in the set of channels, a channel similarity            score for each of the other channels in the set of channels,            wherein a channel similarity score from the perspective of a            first channel for a second channel is calculated based on            the number of common users, in the set of users, that the            first channel and the second channel share;        -   wherein the generated work graph structure includes a set of            channel objects corresponding to the set of channels,            wherein a channel object for a channel includes data            regarding the calculated channel similarity scores            associated with that channel.-   140. The system of embodiment 131, further, comprising:    -   the work graph generating component means, to:        -   calculate, via at least one processor, from the perspective            of each topic in the set of topics, a topic similarity score            for each of the other topics in the set of topics;        -   wherein the generated work graph structure includes a set of            topic objects corresponding to the set of topics, wherein a            topic object for a topic includes data regarding the            calculated topic similarity scores associated with that            topic.-   141. The system of embodiment 131, wherein user priority scores    between two users in the set of users are asymmetric.-   142. The system of embodiment 131, wherein user priority scores    between two users in the set of users are symmetric.-   143. The system of embodiment 131, further, comprising:    -   a machine learning structure generating component means, to:        -   determine, via at least one processor, a ranking application            for which to generate a machine learning structure;        -   determine, via at least one processor, a set of inputs for            the machine learning structure based on the ranking            application, wherein at least some of the inputs in the set            of inputs correspond to work graph data stored in the work            graph data structure;        -   train, via at least one processor, the machine learning            structure using at least some of the work graph data stored            in the work graph data structure; and        -   store, via at least one processor, machine learning            structure parameters of the trained machine learning            structure, wherein the trained machine learning structure is            associated with group level access control data.-   144. The system of embodiment 143, wherein the machine learning    structure is a neural network.-   145. The system of embodiment 143, wherein the ranking application    is any of: ranking metadata access control carrying messages,    ranking people, ranking channels.-   146. A processor-implemented work graph generating method,    comprising:    -   executing processor-implemented work graph generating component        instructions to:        -   obtain, via at least one processor, a work graph generation            request, wherein the work graph generation request includes            group level access control data;        -   determine, via at least one processor, a set of metadata            access control carrying messages, wherein access control            data associated with metadata access control carrying            messages in the set of metadata access control carrying            messages corresponds to the group level access control data;        -   determine, via at least one processor, a set of users,            wherein access control data associated with users in the set            of users corresponds to the group level access control data;        -   calculate, via at least one processor, from the perspective            of each user in the set of users, a user priority score for            each of the other users in the set of users, wherein a user            priority score from the perspective of a first user for a            second user is calculated based on the number of metadata            access control carrying messages, in the set of metadata            access control carrying messages, associated with the second            user that were user-pertinent to the first user;        -   determine, via at least one processor, a set of channels,            wherein access control data associated with channels in the            set of channels corresponds to the group level access            control data;        -   calculate, via at least one processor, from the perspective            of each user in the set of users, a channel priority score            for each of the channels in the set of channels, wherein a            channel priority score from the perspective of a user for a            channel is calculated based on the number of metadata access            control carrying messages, in the set of metadata access            control carrying messages, associated with that channel that            were channel-pertinent to that user;        -   determine, via at least one processor, a set of topics            associated with the set of metadata access control carrying            messages;        -   calculate, via at least one processor, from the perspective            of each user in the set of users, a topic priority score for            each of the topics in the set of topics, wherein a topic            priority score from the perspective of a user for a topic is            calculated based on the number of metadata access control            carrying messages, in the set of metadata access control            carrying messages, associated with that topic that were            topic-pertinent to that user; and        -   generate, via at least one processor, a work graph data            structure that includes a set of user objects corresponding            to the set of users, wherein a user object for a user            includes data regarding the calculated user priority scores,            channel priority scores, and topic priority scores            associated with that user, and wherein the work graph data            structure is associated with group level access control            data.-   147. The method of embodiment 146, wherein the group level access    control data is associated with an organization group.-   148. The method of embodiment 146, wherein the group level access    control data is associated with a team group.-   149. The method of embodiment 146, wherein the set of metadata    access control carrying messages is filtered to exclude metadata    access control carrying messages sent outside a specified time    period.-   150. The method of embodiment 146, wherein the user-pertinent    metadata access control carrying messages are any of: metadata    access control carrying messages from the second user read by the    first user, metadata access control carrying messages from the first    user to the second user, metadata access control carrying messages    from the second user responded to by the first user.-   151. The method of embodiment 150, wherein a response to a metadata    access control carrying message is any of: a reaction to the    metadata access control carrying message, clicking on a link in the    metadata access control carrying message, replying to the metadata    access control carrying message, downloading a file associated with    the metadata access control carrying message, sharing the metadata    access control carrying message to another channel, pinning the    metadata access control carrying message, starring the metadata    access control carrying message.-   152. The method of embodiment 146, wherein the channel-pertinent    metadata access control carrying messages associated with a channel    for a user are any of: metadata access control carrying messages    sent by that user in that channel, metadata access control carrying    messages read by that user in that channel, metadata access control    carrying messages-   153. The method of embodiment 146, wherein the topic-pertinent    metadata access control carrying messages associated with a topic    for a user are any of: metadata access control carrying messages    sent by that user regarding that topic, metadata access control    carrying messages read by that user regarding that topic, metadata    access control carrying messages regarding that topic responded to    by that user.-   154. The method of embodiment 146, further, comprising:    -   executing processor-implemented work graph generating component        instructions to:        -   calculate, via at least one processor, from the perspective            of each channel in the set of channels, a channel similarity            score for each of the other channels in the set of channels,            wherein a channel similarity score from the perspective of a            first channel for a second channel is calculated based on            the number of common users, in the set of users, that the            first channel and the second channel share;        -   wherein the generated work graph structure includes a set of            channel objects corresponding to the set of channels,            wherein a channel object for a channel includes data            regarding the calculated channel similarity scores            associated with that channel.-   155. The method of embodiment 146, further, comprising:    -   executing processor-implemented work graph generating component        instructions to:        -   calculate, via at least one processor, from the perspective            of each topic in the set of topics, a topic similarity score            for each of the other topics in the set of topics;        -   wherein the generated work graph structure includes a set of            topic objects corresponding to the set of topics, wherein a            topic object for a topic includes data regarding the            calculated topic similarity scores associated with that            topic.-   156. The method of embodiment 146, wherein user priority scores    between two users in the set of users are asymmetric.-   157. The method of embodiment 146, wherein user priority scores    between two users in the set of users are symmetric.-   158. The method of embodiment 146, further, comprising:    -   executing processor-implemented machine learning structure        generating component instructions to:        -   determine, via at least one processor, a ranking application            for which to generate a machine learning structure;        -   determine, via at least one processor, a set of inputs for            the machine learning structure based on the ranking            application, wherein at least some of the inputs in the set            of inputs correspond to work graph data stored in the work            graph data structure;        -   train, via at least one processor, the machine learning            structure using at least some of the work graph data stored            in the work graph data structure; and        -   store, via at least one processor, machine learning            structure parameters of the trained machine learning            structure, wherein the trained machine learning structure is            associated with group level access control data.-   159. The method of embodiment 158, wherein the machine learning    structure is a neural network.-   160. The method of embodiment 158, wherein the ranking application    is any of: ranking metadata access control carrying messages,    ranking people, ranking channels.-   201. A ranking determining apparatus, comprising:-   a memory;-   a component collection in the memory, including:    -   a ranking determining component;-   a processor disposed in communication with the memory, and    configured to issue a plurality of processing instructions from the    component collection stored in the memory,    -   wherein the processor issues instructions from the ranking        determining component, stored in the memory, to:        -   obtain, via at least one processor, a ranking request            associated with a user, wherein the ranking request includes            group level access control data, wherein access control data            associated with the user corresponds to the group level            access control data;        -   determine, via at least one processor, a ranking type            associated with the ranking request, wherein the ranking            type indicates a ranking application associated with the            ranking request;        -   retrieve, via at least one processor, a machine learning            structure for the ranking request based on the group level            access control data and the ranking type;        -   determine, via at least one processor, a set of inputs            associated with the machine learning structure, wherein at            least some of the inputs in the set of inputs correspond to            work graph data;        -   obtain, via at least one processor, ranking data, wherein            the ranking data's access control data corresponds to the            group level access control data, wherein the ranking data            includes work graph data associated with the user and a set            of applicable data items;        -   determine, via at least one processor, input values for the            determined set of inputs for each of the applicable data            items;        -   determine, via at least one processor, a ranking score for            each of the applicable data items using the machine learning            structure and the corresponding input values;        -   select, via at least one processor, a set of highest ranked            applicable data items; and        -   facilitate, via at least one processor, generating a user            interface configured to display information regarding the            selected set of highest ranked applicable data items.-   202. The apparatus of embodiment 201, wherein the ranking request is    generated based on a search request associated with the user.-   203. The apparatus of embodiment 202, wherein the ranking request is    one of a plurality of ranking requests generated in response to the    search request, and wherein each of the plurality of generated    ranking requests is associated with a different ranking type.-   204. The apparatus of embodiment 201, wherein the ranking    application is any of: ranking metadata access control carrying    messages, ranking people, ranking channels.-   205. The apparatus of embodiment 201, wherein the machine learning    structure is a neural network.-   206. The apparatus of embodiment 201, wherein the work graph data    associated with the user includes user priority scores, channel    priority scores, and topic priority scores associated with the user.-   207. The apparatus of embodiment 201, wherein the set of applicable    data items includes a set of metadata access control carrying    messages.-   208. The apparatus of embodiment 207, wherein at least some of the    input values for the set of metadata access control carrying    messages are determined based on analysis of the associated indexed    metadata.-   209. The apparatus of embodiment 201, wherein the set of applicable    data items includes a set of users.-   210. The apparatus of embodiment 201, wherein the set of applicable    data items includes a set of channels.-   211. The apparatus of embodiment 201, wherein the set of highest    ranked applicable data items is determined based on a threshold    number of highest ranked applicable data items.-   212. The apparatus of embodiment 201, wherein the set of highest    ranked applicable data items is determined based on a threshold    ranking score.-   213. The apparatus of embodiment 207, wherein the user interface is    configured to display information regarding the highest ranked    metadata access control carrying messages in a channel recap format.-   214. The apparatus of embodiment 209, wherein the user interface is    configured to display information regarding the highest ranked users    in a search results format.-   215. The apparatus of embodiment 210, wherein the user interface is    configured to display information regarding the highest ranked    channels in a channel suggestions format.-   216. A processor-readable ranking determining non-transient physical    medium storing processor-executable components, the components,    comprising:-   a component collection stored in the medium, including:    -   a ranking determining component;    -   wherein the ranking determining component, stored in the medium,        includes processor-issuable instructions to:        -   obtain, via at least one processor, a ranking request            associated with a user, wherein the ranking request includes            group level access control data, wherein access control data            associated with the user corresponds to the group level            access control data;        -   determine, via at least one processor, a ranking type            associated with the ranking request, wherein the ranking            type indicates a ranking application associated with the            ranking request;        -   retrieve, via at least one processor, a machine learning            structure for the ranking request based on the group level            access control data and the ranking type;        -   determine, via at least one processor, a set of inputs            associated with the machine learning structure, wherein at            least some of the inputs in the set of inputs correspond to            work graph data;        -   obtain, via at least one processor, ranking data, wherein            the ranking data's access control data corresponds to the            group level access control data, wherein the ranking data            includes work graph data associated with the user and a set            of applicable data items;        -   determine, via at least one processor, input values for the            determined set of inputs for each of the applicable data            items;        -   determine, via at least one processor, a ranking score for            each of the applicable data items using the machine learning            structure and the corresponding input values;        -   select, via at least one processor, a set of highest ranked            applicable data items; and        -   facilitate, via at least one processor, generating a user            interface configured to display information regarding the            selected set of highest ranked applicable data items.-   217. The medium of embodiment 216, wherein the ranking request is    generated based on a search request associated with the user.-   218. The medium of embodiment 217, wherein the ranking request is    one of a plurality of ranking requests generated in response to the    search request, and wherein each of the plurality of generated    ranking requests is associated with a different ranking type.-   219. The medium of embodiment 216, wherein the ranking application    is any of: ranking metadata access control carrying messages,    ranking people, ranking channels.-   220. The medium of embodiment 216, wherein the machine learning    structure is a neural network.-   221. The medium of embodiment 216, wherein the work graph data    associated with the user includes user priority scores, channel    priority scores, and topic priority scores associated with the user.-   222. The medium of embodiment 216, wherein the set of applicable    data items includes a set of metadata access control carrying    messages.-   223. The medium of embodiment 222, wherein at least some of the    input values for the set of metadata access control carrying    messages are determined based on analysis of the associated indexed    metadata.-   224. The medium of embodiment 216, wherein the set of applicable    data items includes a set of users.-   225. The medium of embodiment 216, wherein the set of applicable    data items includes a set of channels.-   226. The medium of embodiment 216, wherein the set of highest ranked    applicable data items is determined based on a threshold number of    highest ranked applicable data items.-   227. The medium of embodiment 216, wherein the set of highest ranked    applicable data items is determined based on a threshold ranking    score.-   228. The medium of embodiment 222, wherein the user interface is    configured to display information regarding the highest ranked    metadata access control carrying messages in a channel recap format.-   229. The medium of embodiment 224, wherein the user interface is    configured to display information regarding the highest ranked users    in a search results format.-   230. The medium of embodiment 225, wherein the user interface is    configured to display information regarding the highest ranked    channels in a channel suggestions format.-   231. A processor-implemented ranking determining system, comprising:    -   a ranking determining component means, to:        -   obtain, via at least one processor, a ranking request            associated with a user, wherein the ranking request includes            group level access control data, wherein access control data            associated with the user corresponds to the group level            access control data;        -   determine, via at least one processor, a ranking type            associated with the ranking request, wherein the ranking            type indicates a ranking application associated with the            ranking request;        -   retrieve, via at least one processor, a machine learning            structure for the ranking request based on the group level            access control data and the ranking type;        -   determine, via at least one processor, a set of inputs            associated with the machine learning structure, wherein at            least some of the inputs in the set of inputs correspond to            work graph data;        -   obtain, via at least one processor, ranking data, wherein            the ranking data's access control data corresponds to the            group level access control data, wherein the ranking data            includes work graph data associated with the user and a set            of applicable data items;        -   determine, via at least one processor, input values for the            determined set of inputs for each of the applicable data            items;        -   determine, via at least one processor, a ranking score for            each of the applicable data items using the machine learning            structure and the corresponding input values;        -   select, via at least one processor, a set of highest ranked            applicable data items; and        -   facilitate, via at least one processor, generating a user            interface configured to display information regarding the            selected set of highest ranked applicable data items.-   232. The system of embodiment 231, wherein the ranking request is    generated based on a search request associated with the user.-   233. The system of embodiment 232, wherein the ranking request is    one of a plurality of ranking requests generated in response to the    search request, and wherein each of the plurality of generated    ranking requests is associated with a different ranking type.-   234. The system of embodiment 231, wherein the ranking application    is any of: ranking metadata access control carrying messages,    ranking people, ranking channels.-   235. The system of embodiment 231, wherein the machine learning    structure is a neural network.-   236. The system of embodiment 231, wherein the work graph data    associated with the user includes user priority scores, channel    priority scores, and topic priority scores associated with the user.-   237. The system of embodiment 231, wherein the set of applicable    data items includes a set of metadata access control carrying    messages.-   238. The system of embodiment 237, wherein at least some of the    input values for the set of metadata access control carrying    messages are determined based on analysis of the associated indexed    metadata.-   239. The system of embodiment 231, wherein the set of applicable    data items includes a set of users.-   240. The system of embodiment 231, wherein the set of applicable    data items includes a set of channels.-   241. The system of embodiment 231, wherein the set of highest ranked    applicable data items is determined based on a threshold number of    highest ranked applicable data items.-   242. The system of embodiment 231, wherein the set of highest ranked    applicable data items is determined based on a threshold ranking    score.-   243. The system of embodiment 237, wherein the user interface is    configured to display information regarding the highest ranked    metadata access control carrying messages in a channel recap format.-   244. The system of embodiment 239, wherein the user interface is    configured to display information regarding the highest ranked users    in a search results format.-   245. The system of embodiment 240, wherein the user interface is    configured to display information regarding the highest ranked    channels in a channel suggestions format.-   246. A processor-implemented ranking determining method, comprising:    -   executing processor-implemented ranking determining component        instructions to:        -   obtain, via at least one processor, a ranking request            associated with a user, wherein the ranking request includes            group level access control data, wherein access control data            associated with the user corresponds to the group level            access control data;        -   determine, via at least one processor, a ranking type            associated with the ranking request, wherein the ranking            type indicates a ranking application associated with the            ranking request;        -   retrieve, via at least one processor, a machine learning            structure for the ranking request based on the group level            access control data and the ranking type;        -   determine, via at least one processor, a set of inputs            associated with the machine learning structure, wherein at            least some of the inputs in the set of inputs correspond to            work graph data;        -   obtain, via at least one processor, ranking data, wherein            the ranking data's access control data corresponds to the            group level access control data, wherein the ranking data            includes work graph data associated with the user and a set            of applicable data items;        -   determine, via at least one processor, input values for the            determined set of inputs for each of the applicable data            items;        -   determine, via at least one processor, a ranking score for            each of the applicable data items using the machine learning            structure and the corresponding input values;        -   select, via at least one processor, a set of highest ranked            applicable data items; and        -   facilitate, via at least one processor, generating a user            interface configured to display information regarding the            selected set of highest ranked applicable data items.-   247. The method of embodiment 246, wherein the ranking request is    generated based on a search request associated with the user.-   248. The method of embodiment 247, wherein the ranking request is    one of a plurality of ranking requests generated in response to the    search request, and wherein each of the plurality of generated    ranking requests is associated with a different ranking type.-   249. The method of embodiment 246, wherein the ranking application    is any of: ranking metadata access control carrying messages,    ranking people, ranking channels.-   250. The method of embodiment 246, wherein the machine learning    structure is a neural network.-   251. The method of embodiment 246, wherein the work graph data    associated with the user includes user priority scores, channel    priority scores, and topic priority scores associated with-   252. The method of embodiment 246, wherein the set of applicable    data items includes a set of metadata access control carrying    messages.-   253. The method of embodiment 252, wherein at least some of the    input values for the set of metadata access control carrying    messages are determined based on analysis of the associated indexed    metadata.-   254. The method of embodiment 246, wherein the set of applicable    data items includes a set of users.-   255. The method of embodiment 246, wherein the set of applicable    data items includes a set of channels.-   256. The method of embodiment 246, wherein the set of highest ranked    applicable data items is determined based on a threshold number of    highest ranked applicable data items.-   257. The method of embodiment 246, wherein the set of highest ranked    applicable data items is determined based on a threshold ranking    score.-   258. The method of embodiment 252, wherein the user interface is    configured to display information regarding the highest ranked    metadata access control carrying messages in a channel recap format.-   259. The method of embodiment 254, wherein the user interface is    configured to display information regarding the highest ranked users    in a search results format.-   260. The method of embodiment 255, wherein the user interface is    configured to display information regarding the highest ranked    channels in a channel suggestions format.

In order to address various issues and advance the art, the entirety ofthis application for Messaging Search and Management Apparatuses,Methods and Systems (including the Cover Page, Title, Headings, Field,Background, Summary, Brief Description of the Drawings, DetailedDescription, Claims, Abstract, Figures, Appendices, and otherwise)shows, by way of illustration, various embodiments in which the claimedinnovations may be practiced. The advantages and features of theapplication are of a representative sample of embodiments only, and arenot exhaustive and/or exclusive. They are presented only to assist inunderstanding and teach the claimed principles. It should be understoodthat they are not representative of all claimed innovations. As such,certain aspects of the disclosure have not been discussed herein. Thatalternate embodiments may not have been presented for a specific portionof the innovations or that further undescribed alternate embodiments maybe available for a portion is not to be considered a disclaimer of thosealternate embodiments. It will be appreciated that many of thoseundescribed embodiments incorporate the same principles of theinnovations and others are equivalent. Thus, it is to be understood thatother embodiments may be utilized and functional, logical, operational,organizational, structural and/or topological modifications may be madewithout departing from the scope and/or spirit of the disclosure. Assuch, all examples and/or embodiments are deemed to be non-limitingthroughout this disclosure. Further and to the extent any financialand/or investment examples are included, such examples are forillustrative purpose(s) only, and are not, nor should they beinterpreted, as investment advice. Also, no inference should be drawnregarding those embodiments discussed herein relative to those notdiscussed herein other than it is as such for purposes of reducing spaceand repetition. For instance, it is to be understood that the logicaland/or topological structure of any combination of any programcomponents (a component collection), other components, data flow order,logic flow order, and/or any present feature sets as described in thefigures and/or throughout are not limited to a fixed operating orderand/or arrangement, but rather, any disclosed order is exemplary and allequivalents, regardless of order, are contemplated by the disclosure.Similarly, descriptions of embodiments disclosed throughout thisdisclosure, any reference to direction or orientation is merely intendedfor convenience of description and is not intended in any way to limitthe scope of described embodiments. Relative terms such as “lower,”“upper,” “horizontal,” “vertical,” “above,” “below,” “up,” “down,” “top”and “bottom” as well as derivative thereof (e.g., “horizontally,”“downwardly,” “upwardly,” etc.) should not be construed to limitembodiments, and instead, again, are offered for convenience ofdescription of orientation. These relative descriptors are forconvenience of description only and do not require that any embodimentsbe constructed or operated in a particular orientation unless explicitlyindicated as such. Terms such as “attached,” “affixed,” “connected,”“coupled,” “interconnected,” and similar may refer to a relationshipwherein structures are secured or attached to one another eitherdirectly or indirectly through intervening structures, as well as bothmovable or rigid attachments or relationships, unless expresslydescribed otherwise. Furthermore, it is to be understood that suchfeatures are not limited to serial execution, but rather, any number ofthreads, processes, services, servers, and/or the like that may executeasynchronously, concurrently, in parallel, simultaneously,synchronously, and/or the like are contemplated by the disclosure. Assuch, some of these features may be mutually contradictory, in that theycannot be simultaneously present in a single embodiment. Similarly, somefeatures are applicable to one aspect of the innovations, andinapplicable to others. In addition, the disclosure includes otherinnovations not presently claimed. Applicant reserves all rights inthose presently unclaimed innovations including the right to claim suchinnovations, file additional applications, continuations, continuationsin part, divisions, and/or the like thereof. As such, it should beunderstood that advantages, embodiments, examples, functional, features,logical, operational, organizational, structural, topological, and/orother aspects of the disclosure are not to be considered limitations onthe disclosure as defined by the claims or limitations on equivalents tothe claims. It is to be understood that, depending on the particularneeds and/or characteristics of a MSM individual and/or enterprise user,database configuration and/or relational model, data type, datatransmission and/or network framework, syntax structure, and/or thelike, various embodiments of the MSM, may be implemented that enable agreat deal of flexibility and customization. For example, aspects of theMSM may be adapted for operating system and internet operating systemservices. While various embodiments and discussions of the MSM haveincluded internet messaging, however, it is to be understood that theembodiments described herein may be readily configured and/or customizedfor a wide variety of other applications and/or implementations.

What is claimed is:
 1. A work graph generating apparatus, comprising: amemory; a component collection in the memory, including: a work graphgenerating component; a processor disposed in communication with thememory, and configured to issue a plurality of processing instructionsfrom the component collection stored in the memory, wherein theprocessor issues instructions from the work graph generating component,stored in the memory, to: obtain, via at least one processor, a workgraph generation request, wherein the work graph generation requestincludes group level access control data; determine, via at least oneprocessor, a set of metadata access control carrying messages, whereinaccess control data associated with metadata access control carryingmessages in the set of metadata access control carrying messagescorresponds to the group level access control data; determine, via atleast one processor, a set of users, wherein access control dataassociated with users in the set of users corresponds to the group levelaccess control data; calculate, via at least one processor, from theperspective of each user in the set of users, a channel priority scorefor each of the channels in the set of channels, wherein a channelpriority score from the perspective of a user for a channel iscalculated based on the number of metadata access control carryingmessages, in the set of metadata access control carrying messages,associated with that channel that were channel-pertinent to that user,calculate, via at least one processor, from the perspective of eachchannel in the set of channels, a channel similarity score for each ofthe other channels in the set of channels, wherein a channel similarityscore from the perspective of a first channel for a second channel iscalculated based on the number of common users, in the set of users,that the first channel and the second channel share; wherein thegenerated work graph structure includes a set of channel objectscorresponding to the set of channels, wherein a channel object for achannel includes data regarding the calculated channel similarity scoreassociated with that channel; determine, via at least one processor, aset of topics associated with the set of metadata access controlcarrying messages; generate, via at least one processor, a work graphdata structure that includes a set of user objects corresponding to theset of users, wherein a user object for a user includes data regardingthe calculated user priority scores, and channel priority scoresassociated with that user, and wherein the work graph data structure isassociated with group level access control data.
 2. The apparatus ofclaim 1, wherein the group level access control data is associated withan organization group.
 3. The apparatus of claim 1, wherein the grouplevel access control data is associated with a team group.
 4. Theapparatus of claim 1, wherein the set of metadata access controlcarrying messages is filtered to exclude metadata access controlcarrying messages sent outside a specified time period.
 5. The apparatusof claim 1, wherein the user-pertinent metadata access control carryingmessages are any of: metadata access control carrying messages from thesecond user read by the first user, metadata access control carryingmessages from the first user to the second user, metadata access controlcarrying messages from the second user responded to by the first user.6. The apparatus of claim 5, wherein a response to a metadata accesscontrol carrying message is any of: a reaction to the metadata accesscontrol carrying message, clicking on a link in the metadata accesscontrol carrying message, replying to the metadata access controlcarrying message, downloading a file associated with the metadata accesscontrol carrying message, sharing the metadata access control carryingmessage to another channel, pinning the metadata access control carryingmessage, starring the metadata access control carrying message.
 7. Theapparatus of claim 1, wherein the channel-pertinent metadata accesscontrol carrying messages associated with a channel for a user are anyof: metadata access control carrying messages sent by that user in thatchannel, metadata access control carrying messages read by that user inthat channel, metadata access control carrying messages in that channelresponded to by that user.
 8. The apparatus of claim 1, wherein thetopic-pertinent metadata access control carrying messages associatedwith a topic for a user are any of: metadata access control carryingmessages sent by that user regarding that topic, metadata access controlcarrying messages read by that user regarding that topic, metadataaccess control carrying messages regarding that topic responded to bythat user.
 9. The apparatus of claim 1, further, comprising: theprocessor issues instructions from the work graph generating component,stored in the memory, to: calculate, via at least one processor, fromthe perspective of each topic in the set of topics, a topic similarityscore for each of the other topics in the set of topics; wherein thegenerated work graph structure includes a set of topic objectscorresponding to the set of topics, wherein a topic object for a topicincludes data regarding the calculated topic similarity scoresassociated with that topic.
 10. The apparatus of claim 1, wherein userpriority scores between two users in the set of users are asymmetric.11. The apparatus of claim 1, wherein user priority scores between twousers in the set of users are symmetric.
 12. The apparatus of claim 1,further, comprising: a machine learning structure generating componentin the component collection, and the processor issues instructions fromthe machine learning structure generating component, stored in thememory, to: determine, via at least one processor, a ranking applicationfor which to generate a machine learning structure; determine, via atleast one processor, a set of inputs for the machine learning structurebased on the ranking application, wherein at least some of the inputs inthe set of inputs correspond to work graph data stored in the work graphdata structure; train, via at least one processor, the machine learningstructure using at least some of the work graph data stored in the workgraph data structure; and store, via at least one processor, machinelearning structure parameters of the trained machine learning structure,wherein the trained machine learning structure is associated with grouplevel access control data.
 13. The apparatus of claim 12, wherein themachine learning structure is a neural network.
 14. The apparatus ofclaim 12, wherein the ranking application is any of: ranking metadataaccess control carrying messages, ranking people, ranking channels. 15.A processor-readable work graph generating non-transient physical mediumstoring processor-executable components, the components, comprising: acomponent collection stored in the medium, including: a work graphgenerating component; wherein the work graph generating component,stored in the medium, includes processor-issuable instructions to:obtain, via at least one processor, a work graph generation request,wherein the work graph generation request includes group level accesscontrol data; determine, via at least one processor, a set of metadataaccess control carrying messages, wherein access control data associatedwith metadata access control carrying messages in the set of metadataaccess control carrying messages corresponds to the group level accesscontrol data; determine, via at least one processor, a set of users,wherein access control data associated with users in the set of userscorresponds to the group level access control data; calculate, via atleast one processor, from the perspective of each user in the set ofusers, a user priority score for each of the other users in the set ofusers, wherein a user priority score from the perspective of a firstuser for a second user is calculated based on the number of metadataaccess control carrying messages, in the set of metadata access controlcarrying messages, associated with the second user that wereuser-pertinent to the first user, determine, via at least one processor,a set of channels, wherein access control data associated with channelsin the set of channels corresponds to the group level access controldata; calculate, via at least one processor, from the perspective ofeach user in the set of users, a channel priority score for each of thechannels in the set of channels, wherein a channel priority score fromthe perspective of a user for a channel is calculated based on thenumber of metadata access control carrying messages, in the set ofmetadata access control carrying messages, associated with that channelthat were channel-pertinent to that user, calculate, via at least oneprocessor, from the perspective of each channel in the set of channels,a channel similarity score for each of the other channels in the set ofchannels, wherein a channel similarity score from the perspective of afirst channel for a second channel is calculated based on the number ofcommon users, in the set of users, that the first channel and the secondchannel share; wherein the generated work graph structure includes a setof channel objects corresponding to the set of channels, wherein achannel object for a channel includes data regarding the calculatedchannel similarity score associated with that channel; determine, via atleast one processor, a set of topics associated with the set of metadataaccess control carrying messages; generate, via at least one processor,a work graph data structure that includes a set of user objectscorresponding to the set of users, wherein a user object for a userincludes data regarding the calculated user priority scores, and channelpriority scores associated with that user, and wherein the work graphdata structure is associated with group level access control data.
 16. Aprocessor-implemented work graph generating system, comprising: a workgraph generating component means, to: obtain, via at least oneprocessor, a work graph generation request, wherein the work graphgeneration request includes group level access control data; determine,via at least one processor, a set of metadata access control carryingmessages, wherein access control data associated with metadata accesscontrol carrying messages in the set of metadata access control carryingmessages corresponds to the group level access control data; determine,via at least one processor, a set of users, wherein access control dataassociated with users in the set of users corresponds to the group levelaccess control data; calculate, via at least one processor, from theperspective of each user in the set of users, a user priority score foreach of the other users in the set of users, wherein a user priorityscore from the perspective of a first user for a second user iscalculated based on the number of metadata access control carryingmessages, in the set of metadata access control carrying messages,associated with the second user that were user-pertinent to the firstuser, determine, via at least one processor, a set of channels, whereinaccess control data associated with channels in the set of channelscorresponds to the group level access control data; calculate, via atleast one processor, from the perspective of each user in the set ofusers, a channel priority score for each of the channels in the set ofchannels, wherein a channel priority score from the perspective of auser for a channel is calculated based on the number of metadata accesscontrol carrying messages, in the set of metadata access controlcarrying messages, associated with that channel that werechannel-pertinent to that user, calculate, via at least one processor,from the perspective of each channel in the set of channels, a channelsimilarity score for each of the other channels in the set of channels,wherein a channel similarity score from the perspective of a firstchannel for a second channel is calculated based on the number of commonusers, in the set of users, that the first channel and the secondchannel share; wherein the generated work graph structure includes a setof channel objects corresponding to the set of channels, wherein achannel object for a channel includes data regarding the calculatedchannel similarity score associated with that channel; determine, via atleast one processor, a set of topics associated with the set of metadataaccess control carrying messages; generate, via at least one processor,a work graph data structure that includes a set of user objectscorresponding to the set of users, wherein a user object for a userincludes data regarding the calculated user priority scores, and channelpriority scores associated with that user, and wherein the work graphdata structure is associated with group level access control data.
 17. Aprocessor-implemented work graph generating method, comprising:executing processor-implemented work graph generating componentinstructions to: obtain, via at least one processor, a work graphgeneration request, wherein the work graph generation request includesgroup level access control data; determine, via at least one processor,a set of metadata access control carrying messages, wherein accesscontrol data associated with metadata access control carrying messagesin the set of metadata access control carrying messages corresponds tothe group level access control data; determine, via at least oneprocessor, a set of users, wherein access control data associated withusers in the set of users corresponds to the group level access controldata; calculate, via at least one processor, from the perspective ofeach user in the set of users, a user priority score for each of theother users in the set of users, wherein a user priority score from theperspective of a first user for a second user is calculated based on thenumber of metadata access control carrying messages, in the set ofmetadata access control carrying messages, associated with the seconduser that were user-pertinent to the first user, determine, via at leastone processor, a set of channels, wherein access control data associatedwith channels in the set of channels corresponds to the group levelaccess control data; calculate, via at least one processor, from theperspective of each user in the set of users, a channel priority scorefor each of the channels in the set of channels, wherein a channelpriority score from the perspective of a user for a channel iscalculated based on the number of metadata access control carryingmessages, in the set of metadata access control carrying messages,associated with that channel that were channel-pertinent to that user,calculate, via at least one processor, from the perspective of eachchannel in the set of channels, a channel similarity score for each ofthe other channels in the set of channels, wherein a channel similarityscore from the perspective of a first channel for a second channel iscalculated based on the number of common users, in the set of users,that the first channel and the second channel share; wherein thegenerated work graph structure includes a set of channel objectscorresponding to the set of channels, wherein a channel object for achannel includes data regarding the calculated channel similarity scoreassociated with that channel; determine, via at least one processor, aset of topics associated with the set of metadata access controlcarrying messages; generate, via at least one processor, a work graphdata structure that includes a set of user objects corresponding to theset of users, wherein a user object for a user includes data regardingthe calculated user priority scores, and channel priority scoresassociated with that user, and wherein the work graph data structure isassociated with group level access control data.